Techniques for managing distributed computing components

ABSTRACT

Techniques discussed herein are directed to identifying health assessment data of a set of computing instances of a distributed computing system. The health assessment data may be collected from the computing instances and stored in a first distributed cache. When a request for health assessment data for one or more computing instances is received, the health assessment data may be retrieved from the first distributed cache, provided to the requesting entity, and stored in a second distributed cache. A subsequent request may cause new health assessment data to be retrieved from the first distributed cache and compared to the stored data of the second distributed cache. Changes in the health assessment data may be identified and data indicating those changes may be provided in response to the subsequent request. One or more remedial actions may be performed in response to the health assessment data obtained.

CROSS-REFERENCED APPLICATIONS

This application claims priority to provisional application No.63/140,014, entitles “Techniques for Managing Distributed ComputingComponents,” filed Jan. 21, 2021, the entirety of which is incorporatedby reference for all purposes.

BACKGROUND

Distributed computing systems have become increasingly common. Thesesystems may include a computing cluster of connected nodes (e.g.,computers, servers, virtual machines, etc.) that work together in acoordinated fashion to handle various requests (e.g., request to storeand/or retrieve data in a system that maintains a database). Thesecomputing nodes can be utilized by any suitable number of tenants. Asthe number of tasks increases or decreases, the number of connectednodes may be increased or decreased accordingly. Conversely, if thenumber of tasks increases, the number of nodes may be less than what isneeded to efficiently handle the pending tasks, thus introducing greaterlatency for performing the pending tasks. One or more load balancers maybe utilized to manage the execution of tasks by the connected nodes.Understanding the health of the connected nodes is important toefficiently execute the tasks (e.g., create, read, update, and deleteoperations).

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments in accordance with the present disclosure will bedescribed with reference to the drawings, in which:

FIG. 1 illustrates example computing components of a health assessmentservice corresponding to an availability-domain, in accordance with atleast one embodiment.

FIG. 2 illustrates example health assessment service components from aregional view, in accordance with at least one embodiment.

FIG. 3 illustrates a number of distributed caches utilized by adistributed health assessment service, in accordance with at least oneembodiment.

FIG. 4 illustrates an environment for implementing an enhanced loadbalancer, the enhanced load balancer being configured to execute ahealth assessment agent configured to communicate with the healthassessment service, in accordance with at least one embodiment.

FIG. 5 illustrates an example flow for updating health assessment datawith respect to multiple availability domains, in accordance with atleast one embodiment.

FIG. 6 illustrates a block diagram depicting a method for obtaininghealth assessment data, according to at least one embodiment.

FIG. 7 is a high level diagram of a distributed environment showing avirtual or overlay cloud network hosted by a cloud service providerinfrastructure according to certain embodiments.

FIG. 8 depicts a simplified architectural diagram of the physicalcomponents in the physical network within a cloud services providerinfrastructure (CSPI) according to certain embodiments.

FIG. 9 shows an example arrangement within CSPI where a host machine isconnected to multiple network virtualization devices (NVDs) according tocertain embodiments.

FIG. 10 depicts connectivity between a host machine and an NVD forproviding I/O virtualization for supporting multitenancy according tocertain embodiments.

FIG. 11 depicts a simplified block diagram of a physical networkprovided by a CSPI according to certain embodiments.

FIG. 12 is a block diagram illustrating one pattern for implementing acloud infrastructure as a service system, according to at least oneembodiment.

FIG. 13 is a block diagram illustrating another pattern for implementinga cloud infrastructure as a service system, according to at least oneembodiment.

FIG. 14 is a block diagram illustrating another pattern for implementinga cloud infrastructure as a service system, according to at least oneembodiment.

FIG. 15 is a block diagram illustrating another pattern for implementinga cloud infrastructure as a service system, according to at least oneembodiment.

FIG. 16 is a block diagram illustrating an example computer system,according to at least one embodiment.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, specificdetails are set forth in order to provide a thorough understanding ofcertain embodiments. However, it will be apparent that variousembodiments may be practiced without these specific details. The figuresand description are not intended to be restrictive. The word “exemplary”is used herein to mean “serving as an example, instance, orillustration.” Any embodiment or design described herein as “exemplary”is not necessarily to be construed as preferred or advantageous overother embodiments or designs.

The present disclosure relates to a system and method for managing adistributed computing system and health assessment data that indicatesinformation regarding the health of a computing instance of adistributed computing system (e.g., in a cloud-computing system). Adistributed computing system (e.g., a computing cluster) may include anysuitable number of computing instances (e.g., computing nodes, virtualmachines, virtualized containers, etc.) that perform operations in acoordinated manner. In some embodiments, a computing cluster may beprovided in a cloud-computing environment. A “cloud-computingenvironment” may refer to a distributed system in which data storage andcomputing resources are provided over the Internet. As used herein, a“computing instance” (also referred to as “node” and/or “a worker node”)may include a server, a computing device, a virtual machine, avirtualized container, or any suitable physical or virtual computingresource configured to perform operations as part of a computingcluster. By way of example, a computing cluster may include one or moremaster nodes and one or more worker nodes, both being examples of acomputing node of a computing cluster. In some embodiments, a masternode performs any suitable operations related to task assignmentcorresponding to one or more worker nodes, load balancing, nodeprovisioning, node removal, or any suitable operations corresponding tomanaging the computing cluster. A worker node is configured to performoperations corresponding to tasks assigned to it by one or more masternodes. As a non-limiting example, a worker node can perform data storageand/or data retrieval tasks associated with a database at the behest ofa master node that assigns the worker node a particularstorage/retrieval task.

In some embodiments, a distributed Health Assessment (HA) service(referred to as an “HA service” for brevity) may configure any suitablenumber of computing devices with HA data planes (DPs). Each data plane(DP) may include an HA Agent. The HA service or the HA agent may beconfigured to instantiate any suitable number of HA applications. EachHA application may be configured to monitor (e.g., probe for) the healthof a corresponding node/instance of a computing cluster. In someembodiments, the computing device(s) on which the HA service data planesare configured are separate and distinct computing device(s) than thosethat operate the computing cluster being monitored. The heath assessment(HA) data received by the HA applications and compiled/processed by theHA service may be transmitted to a set of load balancers configured tomanage a workflow of the computing cluster. Load balancers may thenutilize the health assessment data in order to distribute workload tasksto healthy backend nodes while avoiding distributing workload tasks tounhealthy nodes.

Similar techniques may be used by a distributed HA service (e.g., thesame HA service discussed above or a different HA service) to monitorthe health of a set of network load balancers. Each network loadbalancer may execute an HA agent and one or more HA applicationsconfigured to probe one or more network load balancers for HA data. Thehealth of each network load balancer can be identified and used todetermine which network load balancers to use for workload distribution.Either, or both, of the HA services (e.g., the one used to monitor thecomputing cluster and the one used to monitor the network loadbalancers) may store the collected HA data in a distributed cache whichcan be access upon request. The system may maintain the last data seenand sent to a client in response to request such that only data that ischanged (e.g., changed from the last transmission to the client) will betransmitted.

Conventional systems may utilize relational databases that may besusceptible to data collisions (e.g., an event that occurs whensimultaneous changes to data associated with identical keys). In orderto avoid collisions, such systems may employ locking mechanisms suchthat changes may only be made by one component at a time. This can causeincreased latency as changes to the same records would need to beperformed in sequence rather than in parallel. Additionally, suchsystems may utilize disk memory.

The techniques discussed herein provide a number of technicalimprovements over conventional systems. The distributed caches describedherein utilize in-memory caches that result in faster data access thanthat of disk memory. Additionally, the distributed nature of the cachesenable parallel queries to be performed on different portions of thedistributed data. Still further, in some embodiments, the data of thedistributed cache may be stored with in a concurrent thread-safelock-free data structure (e.g., a hash array mapped tree structure, aCtrie data structure, etc.). The use of this data structure may supportO(1) algorithmic complexity for atomic, lock-free snapshots. Thus, therisk of data collisions is greatly reduced, or substantially eliminated,when compared to the data collision risk of conventional systems. Asdescribed in detail in connection with FIG. 3, the disclosed techniquesenable HA data to be transmitted in response to request by sending onlydata that has changed from the data that has been previously sent to theclient. Still further, conventional systems may require a client torequest health data for each and every monitored node. However, thetechniques described herein maintain a mapping between a client ID andone or more monitored nodes. This mapping can be utilized such that aclient need only provide its client ID and the HA data (or the changesin HA data) may be gathered and transmitted in a single request, greatlyreducing the number of messages within the network. With theseadvancements, computing operations may be reduced at both the sender(e.g., a client device) and the receiver (e.g., a remote procedure callthread of the system) of the request. Still further, the size of thecaches discussed herein may be dynamically allocated to increase orreduce the storage space as needed at run-time. This enables the systemto dynamically scale the cache(s) to fit the needs of the system byadding more computing instances to those implementing the distributedcache. This can be advantageous over systems that have a static amountof memory where increasing storage would require replacing previouscomputing components with replacements having bigger local memorycomponents.

Moving on to FIG. 1, which illustrates example computing components of ahealth assessment service corresponding to an availability-domain (e.g.,one or more data centers located within a region) of a cloud-computingenvironment 100, in accordance with at least one embodiment. A number ofinstances of the health assessment (HA) service (e.g., HAS 102, aprimary/master, and HAS 104 and HAS 106 that act as one or moresecondary services as depicted in FIG. 1) may be executed to providedata redundancy. Each HA service may expose an API with which thefunctionality of the HA service may be invoked. Each HA service mayoperate a separate service (e.g., a distribution service) that isconfigured to manage a distributed health assessment service over manydata planes that are configured to monitor a computing cluster (e.g.,the computing instance(s) 132). At startup, or another suitable time,multiple instances of the HA service (HAS 102, HAS 104, and HAS 106) maybe started. In some embodiments, HAS 102 is previously designated as amaster, while in other embodiments, the HAS 102-106 may perform anysuitable leader election algorithm to identify a master from among theHA service instances. It should be appreciated that HAS 104 and 106, assecondary services, may be configured to receive any suitable datatransmitted by the HAS 102 and/or HA agents (HA agents 108, 110, and112), any HA service, or any suitable component of the computingcomponents of FIG. 1 in order to distribute load and provide redundancyshould HAS 102 be incapable of executing as master (e.g., the computingdevice on which HAS 102 crashes).

HAS 102 may periodically read from a database (e.g., availability domain(AD) specific data store 107) to identify a set of computing instances(e.g., nodes of the computing instance(s) 132) for which futureworkflows are to be managed with one or more network load balancers(NLBs) (e.g., NLBs 114, 116, and 118). The set of instances may bedynamically configured by tenants by means of interaction with aregional control plane (e.g., the regional control plane 203 of FIG. 2),and persisted to the database (e.g., the AD specific data store 107) bythe control plane. Each NLB may include a NLB data plane (DP) (e.g., NLBDP 120, NLB DP 122, NLB DP 124, as depicted in FIG. 1) that isconfigured to store health assessment data received from an HA service(e.g., a distributed service including HASs 102-106). Each NLB may beconfigured to make decisions regarding load balancing of the set ofinstances (e.g., virtual machines, computing devices, virtualizedcontainers, etc.) to which they are assigned load balancingresponsibilities, based at least in part on the health assessment datareceived from the HA service (e.g., HASs 102-106). It should beappreciated that while a certain number of HA service instances and NLBDPs are shown in FIG. 1, any suitable number of instances may beutilized. Each NLB (e.g., NLB 114, 116, and 118) may expose acorresponding API (e.g., NLB API 134, 136, and 138, respectively) thatmay be utilized to invoke the functionality of each NLB (e.g., todistribute a Create, Read, Update, Delete (CRUD) request to a node ofthe computing instance(s) 132 for processing). In some embodiments, aCRUD request may cause data to be transmitted via an API of an HAservice (e.g., HASs 102-106) to invoke the functionality of the HAService (e.g., the distributed service operating at HASs 102-106).

A number of HA service data plane instances (e.g., HAS DP 126, HAS DP128, and HAS DP 130) may operate on a corresponding computing device(e.g., server computers that are separate/distinct from the computingdevices on which the instances that correspond to the load balancersexecute). In some embodiments, the HA service data plane instances mayoperate on the same computing devices as the network load balancers ofFIG. 1. In some embodiments, the devices that operate HA service dataplanes are different from the computing instances of the computinginstance(s) 132.

The health assessment service operating as master (e.g., HAS 102) isconfigured to read monitoring data from the database (e.g., AD specificdata store 107) to identify the computing instances of the HA servicedata plane (e.g., HAS DP 126-130). The monitoring data may also storethe identities of the computing instances (e.g., nodes of the computinginstance(s) 132) to be monitored, the type of probe (e.g., TCP, HTTP,HTTPS, UDP, etc.) to be utilized for a health status assessment of eachnode. Each instance may be associated with a NLB identifier, an instanceidentifier, and a pod identifier. A pod identifier may identify a groupof instances to which the particular instance belongs. By way ofexample, a pod may include all of the instances associated with a givenclient entity (e.g., a tenant of the cloud-computing/distributedcomputing system) and an identifier corresponding to the client entitymay be associated with each instance corresponding to the client entityto identify which instances belong to that client entity.

Each computing device to be utilized for health assessments may beconfigured with an agent (Health Assessment (HA) agent such as HA agents108-112) that is configured to communicate with the master HA service(e.g., HASs 102-106) to provide health assessment data for the computingdevice on which the HA agent executes. The HA agent may transmit a GETHC configuration (config) message to the master HA service (e.g., HAS102) to request configuration instructions (e.g., which instances (e.g.,computing nodes, virtualized containers, back ends, etc.) of thecomputing instance(s) 132 are assigned to the HA service DPcorresponding to the HA agent, what probes are to be utilized forobtaining health assessment data for each assigned instance (node),etc.). In some embodiments, the HA agent (e.g., HA agent 108) maytransmit within the GET HC config message any suitable health assessmentdata for the computing device on which it executes. By way of example,the HA agent 108 may transmit metrics indicating processing and/orstorage resources (e.g., CPU, memory utilisation, etc.) of the computingdevice on which HAS DP 126 is configured.

The master HA service (e.g., HAS 102) may be configured to execute analgorithm that identifies a set of instances of the computinginstance(s) 132 to assign to each HA service data plane (HAS DP). Thealgorithm may be configured to take into account the available resourcesof the computing device(s) on which each HAS DP is configured, theavailable resources of other computing devices that are also configuredto probe instances of the computing cluster, and the type of probe to beutilized for obtaining a given instance's health assessment data. Insome embodiments, each type of probe may be preassigned a valueindicating an amount of processing needed for executing the probe (e.g.,sending a message via a suitable protocol such as TCP, HTTP, HTTPS, UDP,or the like in order to obtain health assessment data from theinstance). The master HA service (e.g., HAS 102) may assign a set ofinstances to each HAS DP for which the HAS DP is to perform healthassessments/probes. In some embodiments, the set of instances assignedto the HAS DPs may not be mutually exclusive. That is, some instancesmay be assigned to more than one HAS DP.

In response to the GET HC config message, the master (e.g., HAS 102) maybe configured to transmit configuration data (e.g., including instanceassignments, what probe to use for each instance, a schedule or othertiming data corresponding to the probes, etc.) to the various HAS DPs(e.g., to the HA agents 108-112 operating in HAS DPs 126-130). Uponreceipt of the configuration data by the HA agent, a number of HealthAssessment Applications (HAAs) (e.g., HAAs 134, 136, and 138,respectively) may be instantiated in accordance with the configurationdata. Initially, the computing device may not have include any HAapplications. In some embodiments, each HA application may be a virtualmachine configured to execute code to implement the functionality of anHA application. Thus, each HA application may be configured by the HAagent of the same data plane to probe a particular instance of thecomputing cluster. Each HA application may be configured to transmit aprobe (e.g., of the type indicated by the HA Agent and obtained from theHAS 1) to its corresponding instance in accordance with theconfiguration data received and stored by the HA agent of the dataplane. Alternatively, the HAS 102 itself may be configured toinstantiate the various number of HA applications on each HAS DP andconfigure each one to probe a particular instance of the computingcluster in accordance with the configuration data.

Receiving a probe by a computing instance of the computing instance(s)132 may cause the instance to respond with health assessment (HA) data.The particular data fields included in the response may differ dependingon the probe used. The HA application may then be configured to transmitto the master HA service (e.g., HAS 102) directly, or via itscorresponding HA agent, the health assessment data via a POST HC Statusmessage. In some embodiments, “health assessment data” may include anysuitable combination of an identifier for the probed instance (e.g., anode of the computing instance(s) 132), an identifier for the HAS DP(e.g., an identifier for the HAS DP 126), an indicator indicating thehealth of the instance (e.g., healthy/unhealthy), performance metrics,available processing/storage resources of the probed instance, or thelike. In some embodiments, any suitable data received from the instanceby the HAS DP may be transmitted via the POST HC Status message to themaster HA service (e.g., HAS 102).

In some embodiments, the HA applications of each data plane areconfigured to monitor/probe a particular set of nodes of the computingcluster. Since these sets may not be mutually exclusive (e.g., one ormore nodes monitored/probed by the HA applications of HAS DP 126 mayalso be monitored/probed by the HA applications of HAS DP 128 and/or HASDP 130), the master HAS (e.g., HAS 102) may be configured to identify anoverall health status of a given node using the health assessment datareceived (e.g., from each HA agent of the HAS DPs 126-130). By way ofexample, HAS DP 126 and 128 may be configured to monitor/probe a commonnode of the computing instance(s) 132. A corresponding HA application ofthe HAS DPs 126 and 128 may probe the node and provide health assessmentdata to the HA service (e.g., HAS 102) through their corresponding HAagents (HA agent 108 and HA agent 110, respectively). The HA service(e.g., HAS 102) may determine the node's status according to apredetermined rule set. The rule set may specify that a node is healthywhen the health assessment (HA) data from each probing HA applicationindicates the node is healthy or when at least one probe indicates thenode is health and all other probes indicate HA data is absent. The ruleset may further specify that if any HA data indicates an unhealthy node,than the node may be considered unhealthy. The rule set may furtherspecify that if no HA data has been obtained for the node, then thehealth state for the node is unknown. The following table depicts anexample rule set and overall health state determined for a given node(N1).

TABLE 1 Overall Health Node HAA 1 HAA 2 State for Node N1 N1 HealthyHealthy Healthy Healthy Unhealthy Unhealthy Healthy HAD absent HealthyUnhealthy HAD absent Unhealthy HAD absent HAD absent Unknown

In some embodiments, the master HA service (e.g., HAS 102) is configuredto store the HA data in an availability domain (AD) specific data store(e.g., the AD specific data store 107, referred to for brevity as “datastore 107”). In some embodiments, the data store 107 may be adistributed data store that is accessible to other computing componentsof the cloud-computing environment 100 (e.g., the NLBs 114-118, otherHealth Assessment Services, other services, applications, or devices,etc.). In some embodiments, each computing device in the cloud-computingenvironment 100 may be preconfigured with a communication protocol foraccessing the distributed data store (also referred to as a “distributedcache”). In some embodiments, the distributed data store may be anin-memory (e.g., in local memory such as RAM) distributed cache (e.g., acache that is distributed over a number of computing devices of acloud-computing environment 100 such as a computing cluster differentfrom the computing instance(s) 132).

In some embodiments, in response to determining a node is unhealthyand/or the health of a node is unknown a predefined protocol may beexecuted to identify one or more remedial actions. By way of example,when a node is deemed unhealthy, the node may be removed from thecomputing instance(s) 132 from which NLBs 114-118 select for processingtasks. In some embodiments, the HA service (e.g., HA 102) is configuredto transmit a notification via any suitable electronic medium (e.g.,email, SMS text, push notification, and the like) to notify a user(e.g., a network administrator) of the state of the unhealthy node. Insome embodiments, the HA service may be configured to perform anysuitable operations for shutting down the unhealthy node and/or forremoving the unhealthy node from the cluster. In some embodiments, whenthe unhealthy node has been shut down and/or removed, the HA service mayupdate the data store 107 either to remove the status of the nodeentirely or to update the status to indicate the node has been removedand/or shut down. In some embodiments, the HA service may deem the nodeunhealthy if the status of the node remains unknown for a predefinedthreshold period of time (e.g., 5 minutes, 1 hour, etc.). If thethreshold period of time has elapsed since the HA service firstdetermined the status was unknown (or potentially from receipt of thelast status update indicating the node health was unknown), the HAservice may automatically remove the node and/or transmit a notificationthat indicates the period of time for which health status of the nodehas been unknown has breached the predefined threshold.

It should be appreciated that a separate Health Assessment Service(e.g., including every instance of HAS and every HA Agent) may run ineach availability domain to ascertain the health of the instances of thecomputing cluster within that availability domain (AD). By way ofexample, AD Specific HAS 140 may be specific to a particularavailability domain (e.g., “AD1”) while another AD Specific HAS (notdepicted) may be specific to a different availability domain. However,both of the AD Specific HASs may be utilized to monitor the same set ofcomputing nodes (e.g., computing nodes 132) for redundancy purposes.FIG. 2 describes this feature in more detail.

By way of example, FIG. 2 illustrates example health assessment servicecomponents from a regional view, in accordance with at least oneembodiment. As depicted in FIG. 2, a single region (geographical regionsuch as region 202) may include any suitable number of AvailabilityDomains (ADs) (e.g., ADs 204-208, as depicted) which are managed by aregional control plane (e.g., Regional Control Plane 203). As depictedin FIG. 2, each AD may execute its own HA service (e.g., including HAservice instances such as HAS(s) 210, HAS(s) 212, and HAS(s) 214). Eachof the HAS(s) 210-214 may be an example of the HAS 102, HAS 104, and/orHAS 106 of FIG. 1.

In some embodiments, at least one HA service of each AD may configureone or more corresponding data planes (e.g., HAS DP(s) 216, 218, and220, each an example of any of the HAS DPs 126-130 of FIG. 1). Each HASDP can include a corresponding Health Assessment agent (e.g., an HAagent that is an example of the HA agents 108-112 of FIG. 1). Each HASDP can further include any suitable number of Health AssessmentApplications. Each HA application can be configured to monitor/probe forthe health of a corresponding computing instance (e.g., a node) of acomputing cluster (e.g., the computing component(s) 132 of FIG. 1).

The components of each of ADs 204-208 may operate together in a similarmanner as described above in FIG. 1 with respect to a single AD.Although a certain number of ADs, HASs, HAS DP(s), HAAs, HA agents, andAD specific data stores are shown in FIG. 2, any suitable number ofthese components can be utilized.

FIG. 3 illustrates a number of distributed caches utilized by adistributed health assessment service (e.g., the HA service discussedabove in connection with FIGS. 1 and 2), in accordance with at least oneembodiment. The health assessment (HA) data may be received, forexample, by a module of a distributed health assessment service (e.g.,HAS Input/Output (I/O) Modules 302). Each of the I/O modules 302 mayoperate as part of any of the HASs 102-106 of FIG. 1). Each HA serviceinstance of FIG. 1 (e.g., HAS 102, 104, and/or 106) may include acorresponding I/O module that is configured to store the healthassessment (HA) data it received (e.g., from the HA agent(s) and/or HAapplications of FIG. 1). By way of example, I/O module 304 may operateas part of HAS 102, I/O module 306 may operate as part of HAS 104, andI/O module 308 may operate as part of HAS 106 of FIG. 1.

In some embodiments, if the HA data received corresponds to a computingdevice on which an HA agent (e.g., the HA agent 108 of FIG. 1) executes,then the receiving I/O module (e.g., I/O module 306) may be configuredto store the health assessment data in a distributed data store such asthe Agent Health Assessment (HA) Data Cache 310. In some embodiments,the agent HA data cache 310 may be an in-memory distributed data store.The HA data for a given computing device on which an HA agent executes,may be associated with an identifier for the corresponding healthassessment service (HAS) data plane (DP) (e.g., an identifiercorresponding to the HAS DP 126 of FIG. 1) and may include a timestampor the HA data may be timestamped on receipt before the data is stored.In some embodiments, the HAS 102 of FIG. 1 (or any of the instances ofthe HAS (e.g., HASs 104 and/or 106) of FIG. 1) may be configured toutilize the agent HA data cache 310 to identify healthy computingdevices with which health assessments (e.g., transmission of probes,reception of corresponding HA data, etc.) may be performed. Thus, whendetermining what computing devices to assign probing responsibility maydepend, at least in part, on the HA data of the computing devices asindicated in the agent HA data cache 310. The HA service may beconfigured to utilize only computing devices providing a portion of thehealth assessment service data plane (e.g., HAS DPs 126-130 of FIG. 1)that are considered healthy according to predefined rule set (e.g., onesthat have provided a status such as “healthy”, ones that have over athreshold amount of available processing/storage resources, etc.).Additionally, the HA service may balance the load on the HAS DP bydistributing the responsibility for different health assessments basedon the estimated consumption of compute resources (CPU cycles, networkbandwidth, RAM, etc.) and the availability of these resourcescorresponding to each computing device on which each HAS DP isconfigured.

If the HA data received by the I/O module (e.g., I/O module 306) is HAdata for an instance of the computing cluster (e.g., the computinginstance(s) 132 of FIG. 1), then that data may be stored in an in-memorydistributed data store (e.g., HA data cache(s) 312). In some embodimentsall HA data for the computing cluster may be stored in a single HA datacache (e.g., the HA data cache(s) 312 may be a single cache), or eachinstance of the HA data cache(s) 312 may correspond to a particularpod/group of computing nodes of the computing cluster. In someembodiments, the HA data of an instance may be maintained as an entry ofthe HA state cache(s) 312 and/or the health assessment data of aninstance (e.g., a computing node of the computing instance(s) 132) maybe stored with HA data corresponding to other instances in the same pod(e.g., group of associated instances). If stored separately, as depictedin FIG. 3, the HA data may include an identifier of the pod to which theinstance relates. In some embodiments, an entry of the HA data cache mayinclude multiple instances of HA data corresponding a particularcomputing instance of the computing cluster. That is, when multiple HASDPs are configured to obtain HA data for a given instance, each instanceof HA data may be stored in a common entry (e.g., HA data 314 maycorrespond to multiple instances of health data obtained by differentDPs of the same computing instance). In some embodiments, the I/O module(e.g., the I/O module 306) may be configured to identify an overallhealth state for the instance. By way of example, the I/O module may beconfigured to obtain a health state corresponding to each instance of HAdata received for the computing instance. In some embodiments, if any ofthe instances of health assessment data indicates the computing instanceis unhealthy, the I/O module may be configured to set an indicator(e.g., the final instance health) in the entry to indicate the computinginstance is unhealthy. Conversely, if every instance of healthassessment data received indicates that the computing instance ishealthy, the I/O module may be configured to set an indicator (e.g.,final instance health) in the entry of the HA data cache 312 to indicatethe computing instance is healthy. In some embodiments, the finalinstance health may be identified based at least in part on the rulesdescribed above in connection with FIG. 1.

As a non-limiting example, one instance of the HA data cache(s) 312 maybe maintained for a given pod (e.g., a pod corresponding to the nodesassociated with client 1. As depicted in FIG. 3, the pod correspondingto client 1 may include three nodes of the computing instance(s) 132 ofFIG. 1. Each of these computing nodes of the computing cluster can beprobed by any suitable number of the HA applications of FIG. 1 (e.g., anHA application of each HA data plane of FIG. 1). For example, healthassessment data 314 may correspond to a particular computing component,while health assessment data 316 and 318 correspond to differentcomputing components of the computing cluster. The I/O module receivingan instance of the HA data 314 (e.g., I/O module 306) may calculate thefinal instance health for the computing component of the computingcomponent(s) (e.g., when it identifies that all HA agents assigned tothe node have responded with HA data, or at any suitable time).

In some embodiments, the HA data cache(s) 312 may store data in aconcurrent thread-safe, lock-free data structure. By way of example, theHA data cache(s) 312 may utilize a concurrent hash-trie (e.g., a Ctrie)that is configured to support O(1), atomic, lock-free snapshots. Thedata structure utilized for HA data cache(s) 312 may be a non-blockingconcurrent hash array mapped tree structure that is based on single-wordcompare and swap instructions. The data structure may be configured tosupport concurrent lookup, insert and remove operations. In someembodiments, the data structure may utilize an n-bit (e.g., 32 bit,etc.) space for hash values that has low risk of data collisions inrelation to other data structures utilized for storage.

Each instance of the HA service may execute a generation module. By wayof example, the master HA service (e.g., HAS 102 of FIG. 1) may executegeneration module 320. Each generation module may be configured to readfrom a database (e.g., DB 322, an example of the AD specific data store107 of FIG. 1). To identify configuration data indicating one or morepods that individually include one or more computing nodes/instanceswithin a computing cluster such as the computing instance(s) 132 ofFIG. 1. The generation module 320 may utilize the configuration data tocreate pod cache 324. Pod cache 324 may be a distributed or local cache(e.g., local to the master HAS) that maintains a mapping of anidentifier for each instance, the pod ID corresponding to each instance,and the DP identifier corresponding to the HA DP that executes the HAagent (and HA application) assigned to obtain HA data from eachinstance. As discussed above, conventional systems may lack this mappingbetween pod ID, instance, and DP identifier. In those systems, clientsmay be required to transmit a request for each instance. By utilisingthe pod cache 324 as described herein, a single request may be providedwith the client ID. This single request can be utilized to gather all ofthe HA data corresponding to each instances associated with the clientID (as determined from the pod cache 324). In some embodiments, the datamay be aggregated prior to responding to the client to enable the use ofa single response message. Enabling the use of a single request and/or asingle response message greatly reduces the number of messages of thesystem/network which reduces the overall processing burden of the systemas a whole.

The generation module 320 may be configured to create an instance of anHA data cache for each pod (e.g., the HA data cache(s) 312). Thegeneration module 320 may generate the Agent HA data cache 310 thatmaintains HA data for each computing device executing an HA agent. Eachinstance of HA data corresponding to a given HA agent can be associatedwith an HA DP identifier corresponding to the HA DP on which the HAagent executes and a timestamp corresponding to the last HA data updatefor each HA agent.

In some embodiments, an application programming interface (e.g., HA API326) may be provided that enables other services to directly query theHA data cache(s) 312 for health status of an instance and/or a pod. Uponreceiving a request via the HP API 326, an HAS (e.g., HAS 106 of FIG. 1)may obtain aggregated HA data (e.g., HA data 314) for a given node. Insome embodiments, the same API or a different API may be utilized in asimilar manner to obtain HA data (e.g., HA data 314-318) for a givenpod. In some embodiments, the HA data cache(s) 312 may further beconfigured to store HA data corresponding to a particular network loadbalancer (e.g., the NLBs 114-118 of FIG. 1). HA API 328, a same ordifferent API as the HA API 326, may be utilized to retrieve the HA datafor a particular NLB.

In some embodiments, the HAS (e.g., the HASs 102-106 of FIG. 1) mayutilize any suitable number of Remote Procedure Call (RPC) threads(e.g., Google Remote Procedure Call®, etc.) for receiving and processingrequests for health status from a corresponding number of client devices(e.g., user devices operated by or on behalf of a tenant associated withan instance or pod such as clients 1, 2, and 3). As used herein, aRemote Procedure Call may also be referred to as a “consumer thread.” Inat least one example, the client 1 may be associated with pod 1 andclient 2 with pod 2. Each pod may be associated with a predetermined setof one or more instances of the computing cluster). These predeterminedassociations may be stored in a configuration file that is stored in adatabase (e.g., DB 322) that is read periodically by the HAS (e.g., bythe generation module 320). The HAS may read that configuration file andinstantiate and initialize the pod cache 324, an in-memory distributedcache for storing the associations between a pod and its correspondingset of instances and the corresponding data planes assigned to monitoreach instance.

At step 1, an RPC thread (e.g., RPC 330) may receive and process arequest from a client device (e.g., a client device associated withclient 1). If the request corresponds to a single instance, the RPCthread 330 may query the HA data cache(s) 312 for the HA data (includingthe final instance health) of the given instance. If the requestcorresponds to a pod, the RPC may first identify the instancescorresponding to the pod by utilising the pod identifier from therequest as a lookup with pod cache 324. In some embodiments, an RPCthread may obtain the identifiers associated with the instancescorresponding to the pod.

At step 2, once the instance identifiers are known (either from therequest itself, or from retrieval from the pod cache 324), the RPCthread 330 may execute a scatter/gather query to retrieve the HA datafor each instance associated with the pod. The RPC thread 330 may beconfigured to check another in-memory distributed cache (e.g., ClientLast Seen/Sent (CLSS) cache 332) to determine whether the client hasbeen previously sent data for the pod/instance.

Since no health assessment data has been previously been sent to theclient, the RPC thread 330 may store the HA data for each instance ofthe pod in the CLSS cache 332 at step 3. The HA data for each instancemay be stored as a list of last seen data. By storing this data, thesystem may track the last health assessment data for each instance assent to the client.

At step 4, the RPC thread 330 may then transmit to client 1 the HA datafor each instance in pod 1. In some embodiments, the transmission atstep 4 may include separate messages (e.g., one for each instance) or asingle message (e.g., one for the pod). In some embodiments, the clientmay provide an acknowledgement that the data was received (e.g., anacknowledgement for each instance message, an acknowledgement for thepod message).

In some cases, at step 5, the RPC thread 330 may store the data sent tothe client in CLSS cache 332 to maintain an ongoing record of the lastdata sent by the client. Should the client later request the data again(e.g., for the pod or a particular instance), a RPC thread (a newthread) may be configured to retrieve the data last sent to the clientfrom the CLSS cache 332 and retransmit that HA data to the clientdevice. Thus, the CLSS cache 332 may store an entry for every uniqueclient ID/pod ID pair.

At step 6, the client 1 may request updated health assessment data(e.g., for the instances in pod 1, for one or more instances of one ormore pods, etc.). The receiving RPC thread 334 may retrieve new healthassessment data for each instance requested (e.g., each instance in pod1) from the HA data cache(s) 312 at step 7. The RPC thread 334 may thenbe configured to calculate the changes to the HA data by determiningdifferences between the stored health assessment data (e.g., the HA datalast seen for each instance).

At step 8, the RPC thread 334 may be configured to send only the changes(referred to as “change data” indicating the data retrieved from the HAdata cache(s) 312 at step 7 that is different than the last seen datafor that client ID/pod ID pair) to the client device which provides anefficiency benefit over sending the full set of HA data each time. Alist including the data retrieved at step 7 for each instance may bestored in the CLSS cache 332 at step 9. If the client deviceacknowledges receipt, the RPC thread 334 may use the change data toupdate the last sent data in the CLSS cache at step 10. Thus, the lastseen data may include the originally transmitted values (e.g., valuesnot corresponding to the change data that were transmitted at step 4)and data values corresponding to the change data that was transmitted atstep 8, where the change data values overwrite the corresponding valuesof the originally transmitted values thereby maintaining a record of thelast value sent for every data field of the HA data regardless of whenthe value was transmitted (e.g., in the first transmission at step 4 orthe second transmission at step 8).

Although not depicted, in some embodiments, the RPC thread 330 and 334may each correspond to a computing device (e.g., one of one or moreservers, etc.) on which a local instance of the HA data cache(s) 312 maybe maintained. In some embodiments, the corresponding server devices maysubscribe to an event publishing service to be notified on change to theHA data cache(s) 312. Thus, in some embodiments, requests may beprocessed by these server devices without querying the HA data cache(s)312.

In some embodiments, the HA data may be maintained with a version numbercorresponding to a set of changes to the HA data. For example, a firstset of HA data for a given computing component (e.g., one of thecomputing component(s) 132 of FIG. 1) may be labeled and/or associatedwith version 1. Upon obtaining subsequent HA data for the same computingcomponent, the HA data, including any changed data fields may be storedwith a label and/or association to version 2. Each change may correspondto a new version. Thus, when a customer request is received, a snapshotof the HA data at the given time may be maintained in the CLSS cache 332with an association to the version to which that instance of the datacorresponds. When a subsequent request is received from the same client,the RPC thread (e.g., RPC thread 330) may determine whether the versionlast sent to the client corresponds to a different version than theversion currently stored in the HA data cache(s) 312. If so, the data inthe CLSS cache 332 may be updated as described above. However, if theversion that was last seen by the client matches the version stored inthe CLSS cache 332 no data (or an indication that no changes have beenmade) can be transmitted in response to the client's request.

FIG. 4 illustrates an environment for implementing an enhanced loadbalancer (e.g., NLB backend 402), in accordance with at least oneembodiment. Each enhanced load balancer (e.g., the NLB backend 402) maybe associated with a corresponding service VNIC (e.g., an instance ofVNICaaS instance(s) 404). Each VNIC service (e.g., an instance of theVNICaaS instance(s) 404) may be configured to execute NLB module 406,NLB agent 408, one or more health assessment applications (e.g., HAapplication(s) 410), and HA agent 412. NLB Module 406 may be configuredto transmit and receive data from NLB backend 402. In some embodiments,NLB module 406 may transmit HA probes received from HA application(s)410 to NLB backend 402 and receive response data (e.g., healthassessment (HA) data) from the NLB backend 402 which it can then forwardto the HA application that initiated the probe. HA application(s) 410may include one HA application per NLB for each availability domain (AD)in which the NLB operates.

The HA agent 412 may be configured to get HA configuration data(referred to in FIG. 4 as “configuration data”) from the HAS(s) 414(e.g., one or more computing nodes configured to execute an instance ofthe HA service). HA agent 412 may be configured to communicate with anysuitable number of the HA application(s) 410 to obtain HA datacorresponding to any suitable number of network load balancers. In someembodiments, the HA agent 412 may transmit the HA data to the NLB HAS(s)414 which may then store the HA data for each NLB backend in the system(e.g., including NLB backend 402) in a distributed cache (e.g., adistributed cache similar to the HA data cache 312 of FIG. 3). In someembodiments, the HA data for each backend NLB may be stored in the HAdata cache 312 or a separate distributed cache configured to store onlyHA data related to NLBs. The HA Agent 412 may transmit HA data relatedto the VNICaaS instance on which HA agent 412 executes and/or HA datacorresponding to any suitable number of NLBs. Thus, HA application(s)410 and HA agent 412 may be considered similar to the HA applicationsand HA agent of an HA data plane of FIG. 1, but in the context ofmonitoring the health of load balancers that manage workloads of acomputing cluster instead of monitoring the health of the nodes of thecomputing cluster.

NLB Agent 408 may be configured to request/receive NLB backend HA datafrom the NLB HAS(s) 414 (e.g., from a particular NLB HAS operating as amaster). The NLB agent 408 may communicate the HA data to the NLB module406 that may forward the HA data to the NLB backend 402 such that theNLB backend 402 may utilize the HA data to make load balancingdecisions. Although not depicted in FIG. 4, the VNICaaS instance(s) 404could operate as HA DPs like those discussed with respect to FIG. 1.Thus, in some embodiments, the VNICaaS instance(s) 404 could beconfigured to monitor NLB health as well as the health of the computingnodes of a computing cluster (e.g., the computing instance(s) 132 ofFIG. 1).

To create a set of NLBs, routing instance(s) 420 may receive a NLBrequest and forward the request to the NLB API service 422. NLB APIService 422 can handle NLB resource provisioning with asynchronoushandling using WFaaS 424 and a work request model. NLB API Service 422can communicate with NLB HAS(s) 414 to configure the probes. Beforeprovisioning one or more NLBs, the NLB API service 422 may be configuredto verify that the NLB HAS(s) 414 have capacity to run probes of thenumber of NLB backends requested. NLB API service 422 may utilize limits436 (e.g., a service for enforcing resource limits) to enforce anysuitable predefined resource limits. NLB API service 422 may utilizeauthorization service 428 to perform any suitable authenticationoperations related to the request. The NLB API service 422 may push anNLB object to data store 430 for each NLB to be created. The NLB objectmay be persisted in data store 430 and may be accessible by the NLBdistributed service 432 which may be used to provide the data includedin the NLB object to the NLB agent 408 at any suitable time. Anysubsequent updates for an NLB backend may be received by the NLB APIservice 422 and stored in the corresponding NLB object within data store430. The NLB agent 408 and/or NLB distributed service 432 may beconfigured to retrieve those updates at any suitable time. The retrieveddata may be provided by the NLB agent 408 to the NLB backend 402 throughthe NLB module 406. Similarly, the NLB HAS(s) 414 may retrieve anysuitable data from the NLB object(s) at any suitable time.

Continuing on, the NLB API service 422 may call the VCN Control Plane(CP) 434 using a call to APIs exposed by the VCN CP 434. The VCN CP 434may perform any suitable operations for configuring any suitable numberof service VNICs of the VNICaaS instance(s) 404. Additionally, the VCNCP 434 may perform any suitable operations to send information relatedto the service VNIC(s) into the NLB backends (e.g., including NLBbackend 402).

It should be appreciated that NLB HAS(s) 414 may correspond to a singleavailability domain and that a separate NLB HA Fleet Service (notdepicted) may be utilized to maintain multiple HA distributed services,where each HA distributed service (e.g., of which NLB HAS(s) 414 is anexample) corresponds to a single availability domain.

FIG. 5 illustrates an example flow for updating health assessment datacorresponding to a network load balancer with respect to multipleavailability domains (e.g., AD1 and AD2), in accordance with at leastone embodiment.

As can be seen in FIG. 5, separate processes or threads (e.g., WFaaS)may be utilized to report health assessment data for instances tomultiple health assessment control planes (HA CP) (e.g., different setsof HAS(s) 414, where each HAS is considered an HA CP) depending on theavailability domain of the instance(s). For example, an NLB (acorresponding VNICaaS instance) may be configured with multiple HAapplications. One HA application may correspond to AD 1 and another HAapplication may correspond to AD 2. Likewise, One HAS (e.g., HAS (AD1)502) may correspond to AD 1 and another HAS (e.g., HAS (AD1) 504) maycorrespond to AD 2.

At step 1, a NLB may be created using the operations described above inconnection with FIG. 4. For example, a request for creating a number ofNLBs can be forwarded to the NLB API service 422.

At step 2, the NLB API service 422 can execute operations to create arecord within data store 430 to store data related to each requestedNLB. A status for each NLB record may be set to a value indicating theNLB is in the process of being created.

At step 3, the data store 430 may transmit a response to the NLB APIservice 422 confirming that configuration data has been stored. Inresponse, the NLB API service 422 may create a workflow for VCN CP 434and an HAS update and send the workflows to WFaaS 424 to be performed byone or more WFaaS workers (e.g., WFaaS workers 506).

At step 4, upon completing the workflow (e.g., steps 5-12) the WFaaS 424may transmit data to the NLB API service 422, and/or the routinginstance(s) 420 to be displayed to the user 508 (e.g., the user whosubmitted the request at step 1). In some embodiments, the data mayinclude a code (e.g., “200 OK” indicating successful creation of theNLB) and an identifier for the NLBs (e.g., an NLB ID for each NLBcreated).

At step 5, the WFaaS 424 may begin execution of the workflow provided atstep 3. A response may be transmitted back to the WFaaS 424 indicatingthe workflow has begun. The WFaaS 424 may instantiate any suitablenumber of WFaaS workers 506. At step 6, each of the WFaaS workers 506may transmit instructions/data to the VNIC CP 434 to set up acorresponding VNICaaS instance for each NLB requested. Each worker mayreceive a response from the VNIC CP 434 which it may then forward to theWFaaS 424 at step 7.

At steps 8 and 9, a WFaaS worker may perform separate operations forinitializing an HAS for each availability domain. For example, at step8, a WFaaS worker may update an HAS corresponding to AD1 (e.g., HAS(AD1) 502) with information related to a particular NLB operating inAD1. A response may be transmitted to the WFaaS that the HAS for AD1 wassuccessfully or unsuccessfully updated with the NLB information. At step9, a WFaaS worker may update an HAS corresponding to AD2 (e.g., HAS(AD1) 504) with information related to the same NLB that is alsooperating in AD2. The WFaaS may receive a response from a correspondingworker indicating that the HAS for AD2 was successfully orunsuccessfully updated with the NLB information.

At step 10, data may be forwarded from the WFaaS workers 506 to theWFaaS 424 indicating that the HASs for AD1 and AD2 have been updatedwith the information corresponding to the NLB.

At step 11, the WFaaS 424 may execute operations to cause each worker toupdate the data store 430 record corresponding to the NLB to a valueindicating the NLB is active.

At step 12, the data store 430 may provide a response to the workerwhich in turn may provide a response to the WFaaS 424 indicating thestatus of the NLB is complete and the WFaaS may transmit data to theWFaaS workers 506 indicating the workflow is complete.

At step 13, the NLB Fleet Service 432 may poll the data store 430 at anysuitable time for bootstrap and/or update information corresponding tothe NLB. At step 14, the agent operating on the NLB (e.g., NLB agent408) may poll the NLB Fleet Service 432 at any suitable time for NLBdata (e.g., bootstrap data and/or NLB updates).

FIG. 6 illustrates a block diagram depicting a method 600 for obtaininghealth assessment HA data (e.g., HA data corresponding to a number ofcomputing instances/nodes of a computing cluster such as computinginstance(s) 132 of FIG. 1), according to at least one embodiment. Themethod 600 may be performed by a distributed health assessment service(e.g., the HASs 102-106 of FIG. 1, the HAS(s) 210-214 of FIG. 2, etc.).In some embodiments, method 600 may be performed after identifiers for aparticular set of computing instances are provided (by the HAS via anagent) to a health assessment application, which causes the healthassessment application to probe each of the particular set of computinginstances for its corresponding state. In some embodiments, the set ofcomputing instances to be probed by a particular health assessmentapplication may be determined by the software service (the distributedHA service) based at least in part on an estimated resource consumptionfor obtaining the health assessment data corresponding to set ofcomputing instances and an available compute resource capacity of acomputing component hosting the particular health assessmentapplication.

The method 600 may begin at 602, where health assessment datacorresponding to a computing instance of a distributed computingenvironment (e.g., a computing instance/node of the computinginstance(s) 132) may be obtained by a software service (HAS) (e.g., HAS102 of FIG. 1) from a health assessment application (e.g., one of the HAapplications operating at HAS DP 126 of FIG. 1). In this example, thehealth assessment application may include the HA agent and/or the HAAsof FIG. 11. In some embodiments, the computing instance is one of aplurality of computing instances of the distributed computingenvironment (e.g., one of the computing instances/nodes of computinginstance(s) 132, wherein the instances/nodes of computing instance(s)132 are associated with a common pod identifier). In some embodiments,each health assessment application is configured to obtain healthassessment data from a respective subset (e.g., one, two, etc.) of theplurality of computing instances.

At 604, a request (e.g., the request received at step 1 of FIG. 3) forfirst collective health assessment data of a subset (e.g., a podassociated with a client) of the plurality of computing instances may bereceived. The subset of computing instance may be associated with aclient (e.g., client 1). In some embodiments, the request may bereceived by a RPC thread (e.g., RPC thread 330 of FIG. 3). In someembodiments, the receiving component (e.g., the RPC thread 330) mayidentify (e.g., from the pod cache 324 of FIG. 3) the set of one or morecomputing instances using a pod identifier provided in the request. Forexample, an ID for each instance associated with a pod may be retrievedfrom the pod cache 324 of FIG. 3 using a pod identifier. In someembodiments, the first collective health assessment data may includerespective health assessment data of each of the set of one or morecomputing instances (e.g., the HA data at time T1).

At 606, first collective health assessment data (e.g., health assessmentdata for each instance indicated in the request by instance identifierand/or pod identifier) may be obtained (e.g., by the RPC thread 330)from a first distributed cache (HC data cache 312 of FIG. 3). In someembodiments, the first distributed cache comprises an instance of healthassessment data obtained by a first health assessment application,second health assessment data obtained by a second health assessmentapplication, and overall health assessment data for the instancecalculated from the first health assessment data and the second healthassessment data (see table 1 discussed above). The first collectivehealth assessment data corresponds to the subset of computing instancesassociated with the client. In some embodiments, the subset of computinginstances can be identified from a third distributed cache (e.g., podcache 324 of FIG. 3) based at least in part on an identifier associatedwith an entity (e.g., a pod ID associated with a client) associated withthe request.

At 608, the first collective health assessment data for the subset ofcomputing instances may be provided (e.g., from the RPC thread 330 toclient 1 (e.g., to a client device associated with client 1)) inresponse to the request.

At 610, the first collective health assessment data may be stored (e.g.,by the RPC thread 330) in a second distributed cache (e.g., the CLSScache 332 of FIG. 3) as stored health assessment data. In someembodiments, storing the first collective health assessment data in thesecond distributed cache as the stored health assessment data isperformed based at least in part on receiving an indication (e.g., anindication from the client 1) that the first collective healthassessment data has been received.

At 612, a subsequent request for second collective health assessmentdata for the subset of computing instances associated with the clientmay be received (e.g., by a RPC thread 334 of FIG. 3 from a clientdevice such as client 1 of FIG. 3). By way of example, the secondcollective health assessment data may include respective healthassessment data of each of the set of one or more computing instances attime T2. In some embodiments, time T2 may occur after time T1.

At 614, second collective health assessment data corresponding to thesubset of computing instances may be obtained (e.g., by the receivingRPC thread 334) from the first distributed cache (e.g., the HC datacache(s) 312).

At 616, change data indicative of a difference between the firstcollective health assessment data and the second collective healthassessment data is calculated (e.g., by the RPC thread 334).

At 618, the change data is provided (e.g., by the RPC thread 334 to theclient 1) in response to the subsequent request.

At 620, the stored health assessment data is updated (e.g., by the RPCthread 334) with the second collective health assessment data (e.g., thesecond collective health assessment data is used to update the last seendata of CLSS cache 332 for each instance of the pod as described at step9 and 10 of FIG. 3).

In at least one embodiment, the method 600 further comprises maintainingapplication health assessment data for each of the plurality of healthassessment applications in a fourth distributed cache. In someembodiments, the fourth distributed cache can be the same or differentfrom the HA data cache 312 of FIG. 3.

Infrastructure as a service (IaaS) is one particular type of cloudcomputing. IaaS can be configured to provide virtualized computingresources over a public network (e.g., the Internet). In an IaaS model,a cloud computing provider can host the infrastructure components (e.g.,servers, storage devices, network nodes (e.g., hardware), deploymentsoftware, platform virtualization (e.g., a hypervisor layer), or thelike). In some cases, an IaaS provider may also supply a variety ofservices to accompany those infrastructure components (e.g., billing,monitoring, logging, load balancing and clustering, etc.). Thus, asthese services may be policy-driven, IaaS users may be able to implementpolicies to drive load balancing to maintain application availabilityand performance.

In some instances, IaaS customers may access resources and servicesthrough a wide area network (WAN), such as the Internet, and can use thecloud provider's services to install the remaining elements of anapplication stack. For example, the user can log in to the IaaS platformto create virtual machines (VMs), install operating systems (OSs) oneach VM, deploy middleware such as databases, create storage buckets forworkloads and backups, and even install enterprise software into thatVM. Customers can then use the provider's services to perform variousfunctions, including balancing network traffic, troubleshootingapplication issues, monitoring performance, managing disaster recovery,etc.

In most cases, a cloud computing model will require the participation ofa cloud provider. The cloud provider may, but need not be, a third-partyservice that specializes in providing (e.g., offering, renting, selling)IaaS. An entity might also opt to deploy a private cloud, becoming itsown provider of infrastructure services.

In some examples, IaaS deployment is the process of putting a newapplication, or a new version of an application, onto a preparedapplication server or the like. It may also include the process ofpreparing the server (e.g., installing libraries, daemons, etc.). Thisis often managed by the cloud provider, below the hypervisor layer(e.g., the servers, storage, network hardware, and virtualization).Thus, the customer may be responsible for handling (OS), middleware,and/or application deployment (e.g., on self-service virtual machines(e.g., that can be spun up on demand) or the like.

In some examples, IaaS provisioning may refer to acquiring computers orvirtual hosts for use, and even installing needed libraries or serviceson them. In most cases, deployment does not include provisioning, andthe provisioning may need to be performed first.

In some cases, there are two different challenges for IaaS provisioning.First, there is the initial challenge of provisioning the initial set ofinfrastructure before anything is running. Second, there is thechallenge of evolving the existing infrastructure (e.g., adding newservices, changing services, removing services, etc.) once everythinghas been provisioned. In some cases, these two challenges may beaddressed by enabling the configuration of the infrastructure to bedefined declaratively. In other words, the infrastructure (e.g., whatcomponents are needed and how they interact) can be defined by one ormore configuration files. Thus, the overall topology of theinfrastructure (e.g., what resources depend on which, and how they eachwork together) can be described declaratively. In some instances, oncethe topology is defined, a workflow can be generated that creates and/ormanages the different components described in the configuration files.

In some examples, an infrastructure may have many interconnectedelements. For example, there may be one or more virtual private clouds(VPCs) (e.g., a potentially on-demand pool of configurable and/or sharedcomputing resources), also known as a core network. In some examples,there may also be one or more inbound/outbound traffic group rulesprovisioned to define how the inbound and/or outbound traffic of thenetwork will be set up and one or more virtual machines (VMs). Otherinfrastructure elements may also be provisioned, such as a loadbalancer, a database, or the like. As more and more infrastructureelements are desired and/or added, the infrastructure may incrementallyevolve.

In some instances, continuous deployment techniques may be employed toenable deployment of infrastructure code across various virtualcomputing environments. Additionally, the described techniques canenable infrastructure management within these environments. In someexamples, service teams can write code that is desired to be deployed toone or more, but often many, different production environments (e.g.,across various different geographic locations, sometimes spanning theentire world). However, in some examples, the infrastructure on whichthe code will be deployed must first be set up. In some instances, theprovisioning can be done manually, a provisioning tool may be utilizedto provision the resources, and/or deployment tools may be utilized todeploy the code once the infrastructure is provisioned.

FIG. 7 is a block diagram 700 illustrating an example pattern of an IaaSarchitecture, according to at least one embodiment. Service operators702 can be communicatively coupled to a secure host tenancy 704 that caninclude a virtual cloud network (VCN) 706 and a secure host subnet 708.In some examples, the service operators 702 may be using one or moreclient computing devices, which may be portable handheld devices (e.g.,an iPhone®, cellular telephone, an iPad®, computing tablet, a personaldigital assistant (PDA)) or wearable devices (e.g., a Google Glass® headmounted display), running software such as Microsoft Windows Mobile®,and/or a variety of mobile operating systems such as iOS, Windows Phone,Android, BlackBerry 8, Palm OS, and the like, and being Internet,e-mail, short message service (SMS), Blackberry®, or other communicationprotocol enabled. Alternatively, the client computing devices can begeneral purpose personal computers including, by way of example,personal computers and/or laptop computers running various versions ofMicrosoft Windows®, Apple Macintosh®, and/or Linux operating systems.The client computing devices can be workstation computers running any ofa variety of commercially-available UNIX® or UNIX-like operatingsystems, including without limitation the variety of GNU/Linux operatingsystems, such as for example, Google Chrome OS. Alternatively, or inaddition, client computing devices may be any other electronic device,such as a thin-client computer, an Internet-enabled gaming system (e.g.,a Microsoft Xbox gaming console with or without a Kinect® gesture inputdevice), and/or a personal messaging device, capable of communicatingover a network that can access the VCN 706 and/or the Internet.

The VCN 706 can include a local peering gateway (LPG) 710 that can becommunicatively coupled to a secure shell (SSH) VCN 712 via an LPG 710contained in the SSH VCN 712. The SSH VCN 712 can include an SSH subnet714, and the SSH VCN 712 can be communicatively coupled to a controlplane VCN 716 via the LPG 710 contained in the control plane VCN 716.Also, the SSH VCN 712 can be communicatively coupled to a data plane VCN718 via an LPG 710. The control plane VCN 716 and the data plane VCN 718can be contained in a service tenancy 719 that can be owned and/oroperated by the IaaS provider.

The control plane VCN 716 can include a control plane demilitarized zone(DMZ) tier 720 that acts as a perimeter network (e.g., portions of acorporate network between the corporate intranet and external networks).The DMZ-based servers may have restricted responsibilities and help keepbreaches contained. Additionally, the DMZ tier 720 can include one ormore load balancer (LB) subnet(s) 722, a control plane app tier 724 thatcan include app subnet(s) 726, a control plane data tier 728 that caninclude database (DB) subnet(s) 730 (e.g., frontend DB subnet(s) and/orbackend DB subnet(s)). The LB subnet(s) 722 contained in the controlplane DMZ tier 720 can be communicatively coupled to the app subnet(s)726 contained in the control plane app tier 724 and an Internet gateway734 that can be contained in the control plane VCN 716, and the appsubnet(s) 726 can be communicatively coupled to the DB subnet(s) 730contained in the control plane data tier 728 and a service gateway 736and a network address translation (NAT) gateway 738. The control planeVCN 716 can include the service gateway 736 and the NAT gateway 738.

The control plane VCN 716 can include a data plane mirror app tier 740that can include app subnet(s) 726. The app subnet(s) 726 contained inthe data plane mirror app tier 740 can include a virtual networkinterface controller (VNIC) 742 that can execute a compute instance 744.The compute instance 744 can communicatively couple the app subnet(s)726 of the data plane mirror app tier 740 to app subnet(s) 726 that canbe contained in a data plane app tier 746.

The data plane VCN 718 can include the data plane app tier 746, a dataplane DMZ tier 748, and a data plane data tier 750. The data plane DMZtier 748 can include LB subnet(s) 722 that can be communicativelycoupled to the app subnet(s) 726 of the data plane app tier 746 and theInternet gateway 734 of the data plane VCN 718. The app subnet(s) 726can be communicatively coupled to the service gateway 736 of the dataplane VCN 718 and the NAT gateway 738 of the data plane VCN 718. Thedata plane data tier 750 can also include the DB subnet(s) 730 that canbe communicatively coupled to the app subnet(s) 726 of the data planeapp tier 746.

The Internet gateway 734 of the control plane VCN 716 and of the dataplane VCN 718 can be communicatively coupled to a metadata managementservice 752 that can be communicatively coupled to public Internet 754.Public Internet 754 can be communicatively coupled to the NAT gateway738 of the control plane VCN 716 and of the data plane VCN 718. Theservice gateway 736 of the control plane VCN 716 and of the data planeVCN 718 can be communicatively couple to cloud services 756.

In some examples, the service gateway 736 of the control plane VCN 716or of the data plane VCN 718 can make application programming interface(API) calls to cloud services 756 without going through public Internet754. The API calls to cloud services 756 from the service gateway 736can be one-way: the service gateway 736 can make API calls to cloudservices 756, and cloud services 756 can send requested data to theservice gateway 736. But, cloud services 756 may not initiate API callsto the service gateway 736.

In some examples, the secure host tenancy 704 can be directly connectedto the service tenancy 719, which may be otherwise isolated. The securehost subnet 708 can communicate with the SSH subnet 714 through an LPG710 that may enable two-way communication over an otherwise isolatedsystem. Connecting the secure host subnet 708 to the SSH subnet 714 maygive the secure host subnet 708 access to other entities within theservice tenancy 719.

The control plane VCN 716 may allow users of the service tenancy 719 toset up or otherwise provision desired resources. Desired resourcesprovisioned in the control plane VCN 716 may be deployed or otherwiseused in the data plane VCN 718. In some examples, the control plane VCN716 can be isolated from the data plane VCN 718, and the data planemirror app tier 740 of the control plane VCN 716 can communicate withthe data plane app tier 746 of the data plane VCN 718 via VNICs 742 thatcan be contained in the data plane mirror app tier 740 and the dataplane app tier 746.

In some examples, users of the system, or customers, can make requests,for example create, read, update, or delete (CRUD) operations, throughpublic Internet 754 that can communicate the requests to the metadatamanagement service 752. The metadata management service 752 cancommunicate the request to the control plane VCN 716 through theInternet gateway 734. The request can be received by the LB subnet(s)722 contained in the control plane DMZ tier 720. The LB subnet(s) 722may determine that the request is valid, and in response to thisdetermination, the LB subnet(s) 722 can transmit the request to appsubnet(s) 726 contained in the control plane app tier 724. If therequest is validated and requires a call to public Internet 754, thecall to public Internet 754 may be transmitted to the NAT gateway 738that can make the call to public Internet 754. Memory that may bedesired to be stored by the request can be stored in the DB subnet(s)730.

In some examples, the data plane mirror app tier 740 can facilitatedirect communication between the control plane VCN 716 and the dataplane VCN 718. For example, changes, updates, or other suitablemodifications to configuration may be desired to be applied to theresources contained in the data plane VCN 718. Via a VNIC 742, thecontrol plane VCN 716 can directly communicate with, and can therebyexecute the changes, updates, or other suitable modifications toconfiguration to, resources contained in the data plane VCN 718.

In some embodiments, the control plane VCN 716 and the data plane VCN718 can be contained in the service tenancy 719. In this case, the user,or the customer, of the system may not own or operate either the controlplane VCN 716 or the data plane VCN 718. Instead, the IaaS provider mayown or operate the control plane VCN 716 and the data plane VCN 718,both of which may be contained in the service tenancy 719. Thisembodiment can enable isolation of networks that may prevent users orcustomers from interacting with other users', or other customers',resources. Also, this embodiment may allow users or customers of thesystem to store databases privately without needing to rely on publicInternet 754, which may not have a desired level of threat prevention,for storage.

In other embodiments, the LB subnet(s) 722 contained in the controlplane VCN 716 can be configured to receive a signal from the servicegateway 736. In this embodiment, the control plane VCN 716 and the dataplane VCN 718 may be configured to be called by a customer of the IaaSprovider without calling public Internet 754. Customers of the IaaSprovider may desire this embodiment since database(s) that the customersuse may be controlled by the IaaS provider and may be stored on theservice tenancy 719, which may be isolated from public Internet 754.

FIG. 8 is a block diagram 800 illustrating another example pattern of anIaaS architecture, according to at least one embodiment. Serviceoperators 802 (e.g. service operators 702 of FIG. 7) can becommunicatively coupled to a secure host tenancy 804 (e.g. the securehost tenancy 704 of FIG. 7) that can include a virtual cloud network(VCN) 806 (e.g. the VCN 706 of FIG. 7) and a secure host subnet 808(e.g. the secure host subnet 708 of FIG. 7). The VCN 806 can include alocal peering gateway (LPG) 810 (e.g. the LPG 710 of FIG. 7) that can becommunicatively coupled to a secure shell (SSH) VCN 812 (e.g. the SSHVCN 712 of FIG. 7) via an LPG 710 contained in the SSH VCN 812. The SSHVCN 812 can include an SSH subnet 814 (e.g. the SSH subnet 714 of FIG.7), and the SSH VCN 812 can be communicatively coupled to a controlplane VCN 816 (e.g. the control plane VCN 716 of FIG. 7) via an LPG 810contained in the control plane VCN 816. The control plane VCN 816 can becontained in a service tenancy 819 (e.g. the service tenancy 719 of FIG.7), and the data plane VCN 818 (e.g. the data plane VCN 718 of FIG. 7)can be contained in a customer tenancy 821 that may be owned or operatedby users, or customers, of the system.

The control plane VCN 816 can include a control plane DMZ tier 820 (e.g.the control plane DMZ tier 720 of FIG. 7) that can include LB subnet(s)822 (e.g. LB subnet(s) 722 of FIG. 7), a control plane app tier 824(e.g. the control plane app tier 724 of FIG. 7) that can include appsubnet(s) 826 (e.g. app subnet(s) 726 of FIG. 7), a control plane datatier 828 (e.g. the control plane data tier 728 of FIG. 7) that caninclude database (DB) subnet(s) 830 (e.g. similar to DB subnet(s) 730 ofFIG. 7). The LB subnet(s) 822 contained in the control plane DMZ tier820 can be communicatively coupled to the app subnet(s) 826 contained inthe control plane app tier 824 and an Internet gateway 834 (e.g. theInternet gateway 734 of FIG. 7) that can be contained in the controlplane VCN 816, and the app subnet(s) 826 can be communicatively coupledto the DB subnet(s) 830 contained in the control plane data tier 828 anda service gateway 836 (e.g. the service gateway of FIG. 7) and a networkaddress translation (NAT) gateway 838 (e.g. the NAT gateway 738 of FIG.7). The control plane VCN 816 can include the service gateway 836 andthe NAT gateway 838.

The control plane VCN 816 can include a data plane mirror app tier 840(e.g. the data plane mirror app tier 740 of FIG. 7) that can include appsubnet(s) 826. The app subnet(s) 826 contained in the data plane mirrorapp tier 840 can include a virtual network interface controller (VNIC)842 (e.g. the VNIC of 742) that can execute a compute instance 844 (e.g.similar to the compute instance 744 of FIG. 7). The compute instance 844can facilitate communication between the app subnet(s) 826 of the dataplane mirror app tier 840 and the app subnet(s) 826 that can becontained in a data plane app tier 846 (e.g. the data plane app tier 746of FIG. 7) via the VNIC 842 contained in the data plane mirror app tier840 and the VNIC 842 contained in the data plane app tier 846.

The Internet gateway 834 contained in the control plane VCN 816 can becommunicatively coupled to a metadata management service 852 (e.g. themetadata management service 752 of FIG. 7) that can be communicativelycoupled to public Internet 854 (e.g. public Internet 754 of FIG. 7).Public Internet 854 can be communicatively coupled to the NAT gateway838 contained in the control plane VCN 816. The service gateway 836contained in the control plane VCN 816 can be communicatively couple tocloud services 856 (e.g. cloud services 756 of FIG. 7).

In some examples, the data plane VCN 818 can be contained in thecustomer tenancy 821. In this case, the IaaS provider may provide thecontrol plane VCN 816 for each customer, and the IaaS provider may, foreach customer, set up a unique compute instance 844 that is contained inthe service tenancy 819. Each compute instance 844 may allowcommunication between the control plane VCN 816, contained in theservice tenancy 819, and the data plane VCN 818 that is contained in thecustomer tenancy 821. The compute instance 844 may allow resources thatare provisioned in the control plane VCN 816 that is contained in theservice tenancy 819, to be deployed or otherwise used in the data planeVCN 818 that is contained in the customer tenancy 821.

In other examples, the customer of the IaaS provider may have databasesthat live in the customer tenancy 821. In this example, the controlplane VCN 816 can include the data plane mirror app tier 840 that caninclude app subnet(s) 826. The data plane mirror app tier 840 can residein the data plane VCN 818, but the data plane mirror app tier 840 maynot live in the data plane VCN 818. That is, the data plane mirror apptier 840 may have access to the customer tenancy 821, but the data planemirror app tier 840 may not exist in the data plane VCN 818 or be ownedor operated by the customer of the IaaS provider. The data plane mirrorapp tier 840 may be configured to make calls to the data plane VCN 818but may not be configured to make calls to any entity contained in thecontrol plane VCN 816. The customer may desire to deploy or otherwiseuse resources in the data plane VCN 818 that are provisioned in thecontrol plane VCN 816, and the data plane mirror app tier 840 canfacilitate the desired deployment, or other usage of resources, of thecustomer.

In some embodiments, the customer of the IaaS provider can apply filtersto the data plane VCN 818. In this embodiment, the customer candetermine what the data plane VCN 818 can access, and the customer mayrestrict access to public Internet 854 from the data plane VCN 818. TheIaaS provider may not be able to apply filters or otherwise controlaccess of the data plane VCN 818 to any outside networks or databases.Applying filters and controls by the customer onto the data plane VCN818, contained in the customer tenancy 821, can help isolate the dataplane VCN 818 from other customers and from public Internet 854.

In some embodiments, cloud services 856 can be called by the servicegateway 836 to access services that may not exist on public Internet854, on the control plane VCN 816, or on the data plane VCN 818. Theconnection between cloud services 856 and the control plane VCN 816 orthe data plane VCN 818 may not be live or continuous. Cloud services 856may exist on a different network owned or operated by the IaaS provider.Cloud services 856 may be configured to receive calls from the servicegateway 836 and may be configured to not receive calls from publicInternet 854. Some cloud services 856 may be isolated from other cloudservices 856, and the control plane VCN 816 may be isolated from cloudservices 856 that may not be in the same region as the control plane VCN816. For example, the control plane VCN 816 may be located in “Region1,” and cloud service “Deployment 7,” may be located in Region 1 and in“Region 2.” If a call to Deployment 7 is made by the service gateway 836contained in the control plane VCN 816 located in Region 1, the call maybe transmitted to Deployment 7 in Region 1. In this example, the controlplane VCN 816, or Deployment 7 in Region 1, may not be communicativelycoupled to, or otherwise in communication with, Deployment 7 in Region2.

FIG. 9 is a block diagram 900 illustrating another example pattern of anIaaS architecture, according to at least one embodiment. Serviceoperators 902 (e.g. service operators 702 of FIG. 7) can becommunicatively coupled to a secure host tenancy 904 (e.g. the securehost tenancy 704 of FIG. 7) that can include a virtual cloud network(VCN) 906 (e.g. the VCN 706 of FIG. 7) and a secure host subnet 908(e.g. the secure host subnet 708 of FIG. 7). The VCN 906 can include anLPG 910 (e.g. the LPG 710 of FIG. 7) that can be communicatively coupledto an SSH VCN 912 (e.g. the SSH VCN 712 of FIG. 7) via an LPG 910contained in the SSH VCN 912. The SSH VCN 912 can include an SSH subnet914 (e.g. the SSH subnet 714 of FIG. 7), and the SSH VCN 912 can becommunicatively coupled to a control plane VCN 916 (e.g. the controlplane VCN 716 of FIG. 7) via an LPG 910 contained in the control planeVCN 916 and to a data plane VCN 918 (e.g. the data plane 718 of FIG. 7)via an LPG 910 contained in the data plane VCN 918. The control planeVCN 916 and the data plane VCN 918 can be contained in a service tenancy919 (e.g. the service tenancy 719 of FIG. 7).

The control plane VCN 916 can include a control plane DMZ tier 920 (e.g.the control plane DMZ tier 720 of FIG. 7) that can include load balancer(LB) subnet(s) 922 (e.g. LB subnet(s) 722 of FIG. 7), a control planeapp tier 924 (e.g. the control plane app tier 724 of FIG. 7) that caninclude app subnet(s) 926 (e.g. similar to app subnet(s) 726 of FIG. 7),a control plane data tier 928 (e.g. the control plane data tier 728 ofFIG. 7) that can include DB subnet(s) 930. The LB subnet(s) 922contained in the control plane DMZ tier 920 can be communicativelycoupled to the app subnet(s) 926 contained in the control plane app tier924 and to an Internet gateway 934 (e.g. the Internet gateway 734 ofFIG. 7) that can be contained in the control plane VCN 916, and the appsubnet(s) 926 can be communicatively coupled to the DB subnet(s) 930contained in the control plane data tier 928 and to a service gateway936 (e.g. the service gateway of FIG. 7) and a network addresstranslation (NAT) gateway 938 (e.g. the NAT gateway 738 of FIG. 7). Thecontrol plane VCN 916 can include the service gateway 936 and the NATgateway 938.

The data plane VCN 918 can include a data plane app tier 946 (e.g. thedata plane app tier 746 of FIG. 7), a data plane DMZ tier 948 (e.g. thedata plane DMZ tier 748 of FIG. 7), and a data plane data tier 950 (e.g.the data plane data tier 750 of FIG. 7). The data plane DMZ tier 948 caninclude LB subnet(s) 922 that can be communicatively coupled to trustedapp subnet(s) 960 and untrusted app subnet(s) 962 of the data plane apptier 946 and the Internet gateway 934 contained in the data plane VCN918. The trusted app subnet(s) 960 can be communicatively coupled to theservice gateway 936 contained in the data plane VCN 918, the NAT gateway938 contained in the data plane VCN 918, and DB subnet(s) 930 containedin the data plane data tier 950. The untrusted app subnet(s) 962 can becommunicatively coupled to the service gateway 936 contained in the dataplane VCN 918 and DB subnet(s) 930 contained in the data plane data tier950. The data plane data tier 950 can include DB subnet(s) 930 that canbe communicatively coupled to the service gateway 936 contained in thedata plane VCN 918.

The untrusted app subnet(s) 962 can include one or more primary VNICs964(1)-(N) that can be communicatively coupled to tenant virtualmachines (VMs) 966(1)-(N). Each tenant VM 966(1)-(N) can becommunicatively coupled to a respective app subnet 967(1)-(N) that canbe contained in respective container egress VCNs 968(1)-(N) that can becontained in respective customer tenancies 970(1)-(N). Respectivesecondary VNICs 972(1)-(N) can facilitate communication between theuntrusted app subnet(s) 962 contained in the data plane VCN 918 and theapp subnet contained in the container egress VCNs 968(1)-(N). Eachcontainer egress VCNs 968(1)-(N) can include a NAT gateway 938 that canbe communicatively coupled to public Internet 954 (e.g. public Internet754 of FIG. 7).

The Internet gateway 934 contained in the control plane VCN 916 andcontained in the data plane VCN 918 can be communicatively coupled to ametadata management service 952 (e.g. the metadata management system 752of FIG. 7) that can be communicatively coupled to public Internet 954.Public Internet 954 can be communicatively coupled to the NAT gateway938 contained in the control plane VCN 916 and contained in the dataplane VCN 918. The service gateway 936 contained in the control planeVCN 916 and contained in the data plane VCN 918 can be communicativelycouple to cloud services 956.

In some embodiments, the data plane VCN 918 can be integrated withcustomer tenancies 970. This integration can be useful or desirable forcustomers of the IaaS provider in some cases such as a case that maydesire support when executing code. The customer may provide code to runthat may be destructive, may communicate with other customer resources,or may otherwise cause undesirable effects. In response to this, theIaaS provider may determine whether to run code given to the IaaSprovider by the customer.

In some examples, the customer of the IaaS provider may grant temporarynetwork access to the IaaS provider and request a function to beattached to the data plane tier app 946. Code to run the function may beexecuted in the VMs 966(1)-(N), and the code may not be configured torun anywhere else on the data plane VCN 918. Each VM 966(1)-(N) may beconnected to one customer tenancy 970. Respective containers 971(1)-(N)contained in the VMs 966(1)-(N) may be configured to run the code. Inthis case, there can be a dual isolation (e.g., the containers971(1)-(N) running code, where the containers 971(1)-(N) may becontained in at least the VM 966(1)-(N) that are contained in theuntrusted app subnet(s) 962), which may help prevent incorrect orotherwise undesirable code from damaging the network of the IaaSprovider or from damaging a network of a different customer. Thecontainers 971(1)-(N) may be communicatively coupled to the customertenancy 970 and may be configured to transmit or receive data from thecustomer tenancy 970. The containers 971(1)-(N) may not be configured totransmit or receive data from any other entity in the data plane VCN918. Upon completion of running the code, the IaaS provider may kill orotherwise dispose of the containers 971(1)-(N).

In some embodiments, the trusted app subnet(s) 960 may run code that maybe owned or operated by the IaaS provider. In this embodiment, thetrusted app subnet(s) 960 may be communicatively coupled to the DBsubnet(s) 930 and be configured to execute CRUD operations in the DBsubnet(s) 930. The untrusted app subnet(s) 962 may be communicativelycoupled to the DB subnet(s) 930, but in this embodiment, the untrustedapp subnet(s) may be configured to execute read operations in the DBsubnet(s) 930. The containers 971(1)-(N) that can be contained in the VM966(1)-(N) of each customer and that may run code from the customer maynot be communicatively coupled with the DB subnet(s) 930.

In other embodiments, the control plane VCN 916 and the data plane VCN918 may not be directly communicatively coupled. In this embodiment,there may be no direct communication between the control plane VCN 916and the data plane VCN 918. However, communication can occur indirectlythrough at least one method. An LPG 910 may be established by the IaaSprovider that can facilitate communication between the control plane VCN916 and the data plane VCN 918. In another example, the control planeVCN 916 or the data plane VCN 918 can make a call to cloud services 956via the service gateway 936. For example, a call to cloud services 956from the control plane VCN 916 can include a request for a service thatcan communicate with the data plane VCN 918.

FIG. 10 is a block diagram 1000 illustrating another example pattern ofan IaaS architecture, according to at least one embodiment. Serviceoperators 1002 (e.g. service operators 702 of FIG. 7) can becommunicatively coupled to a secure host tenancy 1004 (e.g. the securehost tenancy 704 of FIG. 7) that can include a virtual cloud network(VCN) 1006 (e.g. the VCN 706 of FIG. 7) and a secure host subnet 1008(e.g. the secure host subnet 708 of FIG. 7). The VCN 1006 can include anLPG 1010 (e.g. the LPG 710 of FIG. 7) that can be communicativelycoupled to an SSH VCN 1012 (e.g. the SSH VCN 712 of FIG. 7) via an LPG1010 contained in the SSH VCN 1012. The SSH VCN 1012 can include an SSHsubnet 1014 (e.g. the SSH subnet 714 of FIG. 7), and the SSH VCN 1012can be communicatively coupled to a control plane VCN 1016 (e.g. thecontrol plane VCN 716 of FIG. 7) via an LPG 1010 contained in thecontrol plane VCN 1016 and to a data plane VCN 1018 (e.g. the data plane718 of FIG. 7) via an LPG 1010 contained in the data plane VCN 1018. Thecontrol plane VCN 1016 and the data plane VCN 1018 can be contained in aservice tenancy 1019 (e.g. the service tenancy 719 of FIG. 7).

The control plane VCN 1016 can include a control plane DMZ tier 1020(e.g. the control plane DMZ tier 720 of FIG. 7) that can include LBsubnet(s) 1022 (e.g. LB subnet(s) 722 of FIG. 7), a control plane apptier 1024 (e.g. the control plane app tier 724 of FIG. 7) that caninclude app subnet(s) 1026 (e.g. app subnet(s) 726 of FIG. 7), a controlplane data tier 1028 (e.g. the control plane data tier 728 of FIG. 7)that can include DB subnet(s) 1030 (e.g. DB subnet(s) 930 of FIG. 9).The LB subnet(s) 1022 contained in the control plane DMZ tier 1020 canbe communicatively coupled to the app subnet(s) 1026 contained in thecontrol plane app tier 1024 and to an Internet gateway 1034 (e.g. theInternet gateway 734 of FIG. 7) that can be contained in the controlplane VCN 1016, and the app subnet(s) 1026 can be communicativelycoupled to the DB subnet(s) 1030 contained in the control plane datatier 1028 and to a service gateway 1036 (e.g. the service gateway ofFIG. 7) and a network address translation (NAT) gateway 1038 (e.g. theNAT gateway 738 of FIG. 7). The control plane VCN 1016 can include theservice gateway 1036 and the NAT gateway 1038.

The data plane VCN 1018 can include a data plane app tier 1046 (e.g. thedata plane app tier 746 of FIG. 7), a data plane DMZ tier 1048 (e.g. thedata plane DMZ tier 748 of FIG. 7), and a data plane data tier 1050(e.g. the data plane data tier 750 of FIG. 7). The data plane DMZ tier1048 can include LB subnet(s) 1022 that can be communicatively coupledto trusted app subnet(s) 1060 (e.g. trusted app subnet(s) 960 of FIG. 9)and untrusted app subnet(s) 1062 (e.g. untrusted app subnet(s) 962 ofFIG. 9) of the data plane app tier 1046 and the Internet gateway 1034contained in the data plane VCN 1018. The trusted app subnet(s) 1060 canbe communicatively coupled to the service gateway 1036 contained in thedata plane VCN 1018, the NAT gateway 1038 contained in the data planeVCN 1018, and DB subnet(s) 1030 contained in the data plane data tier1050. The untrusted app subnet(s) 1062 can be communicatively coupled tothe service gateway 1036 contained in the data plane VCN 1018 and DBsubnet(s) 1030 contained in the data plane data tier 1050. The dataplane data tier 1050 can include DB subnet(s) 1030 that can becommunicatively coupled to the service gateway 1036 contained in thedata plane VCN 1018.

The untrusted app subnet(s) 1062 can include primary VNICs 1064(1)-(N)that can be communicatively coupled to tenant virtual machines (VMs)1066(1)-(N) residing within the untrusted app subnet(s) 1062. Eachtenant VM 1066(1)-(N) can run code in a respective container1067(1)-(N), and be communicatively coupled to an app subnet 1026 thatcan be contained in a data plane app tier 1046 that can be contained ina container egress VCN 1068. Respective secondary VNICs 1072(1)-(N) canfacilitate communication between the untrusted app subnet(s) 1062contained in the data plane VCN 1018 and the app subnet contained in thecontainer egress VCN 1068. The container egress VCN can include a NATgateway 1038 that can be communicatively coupled to public Internet 1054(e.g. public Internet 754 of FIG. 7).

The Internet gateway 1034 contained in the control plane VCN 1016 andcontained in the data plane VCN 1018 can be communicatively coupled to ametadata management service 1052 (e.g. the metadata management system752 of FIG. 7) that can be communicatively coupled to public Internet1054. Public Internet 1054 can be communicatively coupled to the NATgateway 1038 contained in the control plane VCN 1016 and contained inthe data plane VCN 1018. The service gateway 1036 contained in thecontrol plane VCN 1016 and contained in the data plane VCN 1018 can becommunicatively couple to cloud services 1056.

In some examples, the pattern illustrated by the architecture of blockdiagram 1000 of FIG. 10 may be considered an exception to the patternillustrated by the architecture of block diagram 900 of FIG. 9 and maybe desirable for a customer of the IaaS provider if the IaaS providercannot directly communicate with the customer (e.g., a disconnectedregion). The respective containers 1067(1)-(N) that are contained in theVMs 1066(1)-(N) for each customer can be accessed in real-time by thecustomer. The containers 1067(1)-(N) may be configured to make calls torespective secondary VNICs 1072(1)-(N) contained in app subnet(s) 1026of the data plane app tier 1046 that can be contained in the containeregress VCN 1068. The secondary VNICs 1072(1)-(N) can transmit the callsto the NAT gateway 1038 that may transmit the calls to public Internet1054. In this example, the containers 1067(1)-(N) that can be accessedin real-time by the customer can be isolated from the control plane VCN1016 and can be isolated from other entities contained in the data planeVCN 1018. The containers 1067(1)-(N) may also be isolated from resourcesfrom other customers.

In other examples, the customer can use the containers 1067(1)-(N) tocall cloud services 1056. In this example, the customer may run code inthe containers 1067(1)-(N) that requests a service from cloud services1056. The containers 1067(1)-(N) can transmit this request to thesecondary VNICs 1072(1)-(N) that can transmit the request to the NATgateway that can transmit the request to public Internet 1054. PublicInternet 1054 can transmit the request to LB subnet(s) 1022 contained inthe control plane VCN 1016 via the Internet gateway 1034. In response todetermining the request is valid, the LB subnet(s) can transmit therequest to app subnet(s) 1026 that can transmit the request to cloudservices 1056 via the service gateway 1036.

It should be appreciated that IaaS architectures 700, 800, 900, 1000depicted in the figures may have other components than those depicted.Further, the embodiments shown in the figures are only some examples ofa cloud infrastructure system that may incorporate an embodiment of thedisclosure. In some other embodiments, the IaaS systems may have more orfewer components than shown in the figures, may combine two or morecomponents, or may have a different configuration or arrangement ofcomponents.

In certain embodiments, the IaaS systems described herein may include asuite of applications, middleware, and database service offerings thatare delivered to a customer in a self-service, subscription-based,elastically scalable, reliable, highly available, and secure manner. Anexample of such an IaaS system is the Oracle Cloud Infrastructure (OCI)provided by the present assignee.

FIG. 11 illustrates an example computer system 1100, in which variousembodiments may be implemented. The system 1100 may be used to implementany of the computer systems described above. As shown in the figure,computer system 1100 includes a processing unit 1104 that communicateswith a number of peripheral subsystems via a bus subsystem 1102. Theseperipheral subsystems may include a processing acceleration unit 1106,an I/O subsystem 1108, a storage subsystem 1118, and a communicationssubsystem 1124. Storage subsystem 1118 includes tangiblecomputer-readable storage media 1122 and a system memory 1110.

Bus subsystem 1102 provides a mechanism for letting the variouscomponents and subsystems of computer system 1100 communicate with eachother as intended. Although bus subsystem 1102 is shown schematically asa single bus, alternative embodiments of the bus subsystem may utilizemultiple buses. Bus subsystem 1102 may be any of several types of busstructures including a memory bus or memory controller, a peripheralbus, and a local bus using any of a variety of bus architectures. Forexample, such architectures may include an Industry StandardArchitecture (ISA) bus, Micro Channel Architecture (MCA) bus, EnhancedISA (EISA) bus, Video Electronics Standards Association (VESA) localbus, and Peripheral Component Interconnect (PCI) bus, which can beimplemented as a Mezzanine bus manufactured to the IEEE P1386.1standard.

Processing unit 1104, which can be implemented as one or more integratedcircuits (e.g., a conventional microprocessor or microcontroller),controls the operation of computer system 1100. One or more processorsmay be included in processing unit 1104. These processors may includesingle core or multicore processors. In certain embodiments, processingunit 1104 may be implemented as one or more independent processing units1132 and/or 1134 with single or multicore processors included in eachprocessing unit. In other embodiments, processing unit 1104 may also beimplemented as a quad-core processing unit formed by integrating twodual-core processors into a single chip.

In various embodiments, processing unit 1104 can execute a variety ofprograms in response to program code and can maintain multipleconcurrently executing programs or processes. At any given time, some orall of the program code to be executed can be resident in processor(s)1104 and/or in storage subsystem 1118. Through suitable programming,processor(s) 1104 can provide various functionalities described above.Computer system 1100 may additionally include a processing accelerationunit 1106, which can include a digital signal processor (DSP), aspecial-purpose processor, and/or the like.

I/O subsystem 1108 may include user interface input devices and userinterface output devices. User interface input devices may include akeyboard, pointing devices such as a mouse or trackball, a touchpad ortouch screen incorporated into a display, a scroll wheel, a click wheel,a dial, a button, a switch, a keypad, audio input devices with voicecommand recognition systems, microphones, and other types of inputdevices. User interface input devices may include, for example, motionsensing and/or gesture recognition devices such as the Microsoft Kinect®motion sensor that enables users to control and interact with an inputdevice, such as the Microsoft Xbox® 360 game controller, through anatural user interface using gestures and spoken commands. Userinterface input devices may also include eye gesture recognition devicessuch as the Google Glass® blink detector that detects eye activity(e.g., ‘blinking’ while taking pictures and/or making a menu selection)from users and transforms the eye gestures as input into an input device(e.g., Google Glass®). Additionally, user interface input devices mayinclude voice recognition sensing devices that enable users to interactwith voice recognition systems (e.g., Siri® navigator), through voicecommands.

User interface input devices may also include, without limitation, threedimensional (3D) mice, joysticks or pointing sticks, gamepads andgraphic tablets, and audio/visual devices such as speakers, digitalcameras, digital camcorders, portable media players, webcams, imagescanners, fingerprint scanners, barcode reader 3D scanners, 3D printers,laser rangefinders, and eye gaze tracking devices. Additionally, userinterface input devices may include, for example, medical imaging inputdevices such as computed tomography, magnetic resonance imaging,position emission tomography, medical ultrasonography devices. Userinterface input devices may also include, for example, audio inputdevices such as MIDI keyboards, digital musical instruments and thelike.

User interface output devices may include a display subsystem, indicatorlights, or non-visual displays such as audio output devices, etc. Thedisplay subsystem may be a cathode ray tube (CRT), a flat-panel device,such as that using a liquid crystal display (LCD) or plasma display, aprojection device, a touch screen, and the like. In general, use of theterm “output device” is intended to include all possible types ofdevices and mechanisms for outputting information from computer system1100 to a user or other computer. For example, user interface outputdevices may include, without limitation, a variety of display devicesthat visually convey text, graphics and audio/video information such asmonitors, printers, speakers, headphones, automotive navigation systems,plotters, voice output devices, and modems.

Computer system 1100 may comprise a storage subsystem 1118 thatcomprises software elements, shown as being currently located within asystem memory 1110. System memory 1110 may store program instructionsthat are loadable and executable on processing unit 1104, as well asdata generated during the execution of these programs.

Depending on the configuration and type of computer system 1100, systemmemory 1110 may be volatile (such as random access memory (RAM)) and/ornon-volatile (such as read-only memory (ROM), flash memory, etc.) TheRAM typically contains data and/or program modules that are immediatelyaccessible to and/or presently being operated and executed by processingunit 1104. In some implementations, system memory 1110 may includemultiple different types of memory, such as static random access memory(SRAM) or dynamic random access memory (DRAM). In some implementations,a basic input/output system (BIOS), containing the basic routines thathelp to transfer information between elements within computer system1100, such as during start-up, may typically be stored in the ROM. Byway of example, and not limitation, system memory 1110 also illustratesapplication programs 1112, which may include client applications, Webbrowsers, mid-tier applications, relational database management systems(RDBMS), etc., program data 1114, and an operating system 1116. By wayof example, operating system 1116 may include various versions ofMicrosoft Windows®, Apple Macintosh®, and/or Linux operating systems, avariety of commercially-available UNIX® or UNIX-like operating systems(including without limitation the variety of GNU/Linux operatingsystems, the Google Chrome® OS, and the like) and/or mobile operatingsystems such as iOS, Windows® Phone, Android® OS, BlackBerry® 11 OS, andPalm® OS operating systems.

Storage subsystem 1118 may also provide a tangible computer-readablestorage medium for storing the basic programming and data constructsthat provide the functionality of some embodiments. Software (programs,code modules, instructions) that when executed by a processor providethe functionality described above may be stored in storage subsystem1118. These software modules or instructions may be executed byprocessing unit 1104. Storage subsystem 1118 may also provide arepository for storing data used in accordance with the presentdisclosure.

Storage subsystem 1100 may also include a computer-readable storagemedia reader 1120 that can further be connected to computer-readablestorage media 1122. Together and, optionally, in combination with systemmemory 1110, computer-readable storage media 1122 may comprehensivelyrepresent remote, local, fixed, and/or removable storage devices plusstorage media for temporarily and/or more permanently containing,storing, transmitting, and retrieving computer-readable information.

Computer-readable storage media 1122 containing code, or portions ofcode, can also include any appropriate media known or used in the art,including storage media and communication media, such as but not limitedto, volatile and non-volatile, removable and non-removable mediaimplemented in any method or technology for storage and/or transmissionof information. This can include tangible computer-readable storagemedia such as RAM, ROM, electronically erasable programmable ROM(EEPROM), flash memory or other memory technology, CD-ROM, digitalversatile disk (DVD), or other optical storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or other tangible computer readable media. This can also includenontangible computer-readable media, such as data signals, datatransmissions, or any other medium which can be used to transmit thedesired information and which can be accessed by computing system 1100.

By way of example, computer-readable storage media 1122 may include ahard disk drive that reads from or writes to non-removable, nonvolatilemagnetic media, a magnetic disk drive that reads from or writes to aremovable, nonvolatile magnetic disk, and an optical disk drive thatreads from or writes to a removable, nonvolatile optical disk such as aCD ROM, DVD, and Blu-Ray® disk, or other optical media.Computer-readable storage media 1122 may include, but is not limited to,Zip® drives, flash memory cards, universal serial bus (USB) flashdrives, secure digital (SD) cards, DVD disks, digital video tape, andthe like. Computer-readable storage media 1122 may also include,solid-state drives (SSD) based on non-volatile memory such asflash-memory based SSDs, enterprise flash drives, solid state ROM, andthe like, SSDs based on volatile memory such as solid state RAM, dynamicRAM, static RAM, DRAM-based SSDs, magnetoresistive RAM (MRAM) SSDs, andhybrid SSDs that use a combination of DRAM and flash memory based SSDs.The disk drives and their associated computer-readable media may providenon-volatile storage of computer-readable instructions, data structures,program modules, and other data for computer system 1100.

Communications subsystem 1124 provides an interface to other computersystems and networks. Communications subsystem 1124 serves as aninterface for receiving data from and transmitting data to other systemsfrom computer system 1100. For example, communications subsystem 1124may enable computer system 1100 to connect to one or more devices viathe Internet. In some embodiments communications subsystem 1124 caninclude radio frequency (RF) transceiver components for accessingwireless voice and/or data networks (e.g., using cellular telephonetechnology, advanced data network technology, such as 3G, 4G or EDGE(enhanced data rates for global evolution), WiFi (IEEE 802.11 familystandards, or other mobile communication technologies, or anycombination thereof), global positioning system (GPS) receivercomponents, and/or other components. In some embodiments communicationssubsystem 1124 can provide wired network connectivity (e.g., Ethernet)in addition to or instead of a wireless interface.

In some embodiments, communications subsystem 1124 may also receiveinput communication in the form of structured and/or unstructured datafeeds 1126, event streams 1128, event updates 1130, and the like onbehalf of one or more users who may use computer system 1100.

By way of example, communications subsystem 1124 may be configured toreceive data feeds 1126 in real-time from users of social networksand/or other communication services such as Twitter® feeds, Facebook®updates, web feeds such as Rich Site Summary (RSS) feeds, and/orreal-time updates from one or more third party information sources.

Additionally, communications subsystem 1124 may also be configured toreceive data in the form of continuous data streams, which may includeevent streams 1128 of real-time events and/or event updates 1130, thatmay be continuous or unbounded in nature with no explicit end. Examplesof applications that generate continuous data may include, for example,sensor data applications, financial tickers, network performancemeasuring tools (e.g. network monitoring and traffic managementapplications), clickstream analysis tools, automobile trafficmonitoring, and the like.

Communications subsystem 1124 may also be configured to output thestructured and/or unstructured data feeds 1126, event streams 1128,event updates 1130, and the like to one or more databases that may be incommunication with one or more streaming data source computers coupledto computer system 1100.

Computer system 1100 can be one of various types, including a handheldportable device (e.g., an iPhone® cellular phone, an iPad® computingtablet, a PDA), a wearable device (e.g., a Google Glass® head mounteddisplay), a PC, a workstation, a mainframe, a kiosk, a server rack, orany other data processing system.

As noted above, infrastructure as a service (IaaS) is one particulartype of cloud computing. IaaS can be configured to provide virtualizedcomputing resources over a public network (e.g., the Internet). In anIaaS model, a cloud computing provider can host the infrastructurecomponents (e.g., servers, storage devices, network nodes (e.g.,hardware), deployment software, platform virtualization (e.g., ahypervisor layer), or the like). In some cases, an IaaS provider mayalso supply a variety of services to accompany those infrastructurecomponents (e.g., billing, monitoring, logging, security, load balancingand clustering, etc.). Thus, as these services may be policy-driven,IaaS users may be able to implement policies to drive load balancing tomaintain application availability and performance.

In some instances, IaaS customers may access resources and servicesthrough a wide area network (WAN), such as the Internet, and can use thecloud provider's services to install the remaining elements of anapplication stack. For example, the user can log in to the IaaS platformto create virtual machines (VMs), install operating systems (OSs) oneach VM, deploy middleware such as databases, create storage buckets forworkloads and backups, and even install enterprise software into thatVM. Customers can then use the provider's services to perform variousfunctions, including balancing network traffic, troubleshootingapplication issues, monitoring performance, managing disaster recovery,etc.

In most cases, a cloud computing model will require the participation ofa cloud provider. The cloud provider may, but need not be, a third-partyservice that specializes in providing (e.g., offering, renting, selling)IaaS. An entity might also opt to deploy a private cloud, becoming itsown provider of infrastructure services.

In some examples, IaaS deployment is the process of putting a newapplication, or a new version of an application, onto a preparedapplication server or the like. It may also include the process ofpreparing the server (e.g., installing libraries, daemons, etc.). Thisis often managed by the cloud provider, below the hypervisor layer(e.g., the servers, storage, network hardware, and virtualization).Thus, the customer may be responsible for handling (OS), middleware,and/or application deployment (e.g., on self-service virtual machines(e.g., that can be spun up on demand) or the like.

In some examples, IaaS provisioning may refer to acquiring computers orvirtual hosts for use, and even installing needed libraries or serviceson them. In most cases, deployment does not include provisioning, andthe provisioning may need to be performed first.

In some cases, there are two different problems for IaaS provisioning.First, there is the initial challenge of provisioning the initial set ofinfrastructure before anything is running. Second, there is thechallenge of evolving the existing infrastructure (e.g., adding newservices, changing services, removing services, etc.) once everythinghas been provisioned. In some cases, these two challenges may beaddressed by enabling the configuration of the infrastructure to bedefined declaratively. In other words, the infrastructure (e.g., whatcomponents are needed and how they interact) can be defined by one ormore configuration files. Thus, the overall topology of theinfrastructure (e.g., what resources depend on which, and how they eachwork together) can be described declaratively. In some instances, oncethe topology is defined, a workflow can be generated that creates and/ormanages the different components described in the configuration files.

In some examples, an infrastructure may have many interconnectedelements. For example, there may be one or more virtual private clouds(VPCs) (e.g., a potentially on-demand pool of configurable and/or sharedcomputing resources), also known as a core network. In some examples,there may also be one or more security group rules provisioned to definehow the security of the network will be set up and one or more virtualmachines (VMs). Other infrastructure elements may also be provisioned,such as a load balancer, a database, or the like. As more and moreinfrastructure elements are desired and/or added, the infrastructure mayincrementally evolve.

In some instances, continuous deployment techniques may be employed toenable deployment of infrastructure code across various virtualcomputing environments. Additionally, the described techniques canenable infrastructure management within these environments. In someexamples, service teams can write code that is desired to be deployed toone or more, but often many, different production environments (e.g.,across various different geographic locations, sometimes spanning theentire world). However, in some examples, the infrastructure on whichthe code will be deployed must first be set up. In some instances, theprovisioning can be done manually, a provisioning tool may be utilizedto provision the resources, and/or deployment tools may be utilized todeploy the code once the infrastructure is provisioned.

FIG. 12 is a block diagram 1200 illustrating an example pattern of anIaaS architecture, according to at least one embodiment. Serviceoperators 1202 can be communicatively coupled to a secure host tenancy1204 that can include a virtual cloud network (VCN) 1206 and a securehost subnet 1208. In some examples, the service operators 1202 may beusing one or more client computing devices, which may be portablehandheld devices (e.g., an iPhone®, cellular telephone, an iPad®,computing tablet, a personal digital assistant (PDA)) or wearabledevices (e.g., a Google Glass® head mounted display), running softwaresuch as Microsoft Windows Mobile®, and/or a variety of mobile operatingsystems such as iOS, Windows Phone, Android, BlackBerry 8, Palm OS, andthe like, and being Internet, e-mail, short message service (SMS),Blackberry®, or other communication protocol enabled. Alternatively, theclient computing devices can be general purpose personal computersincluding, by way of example, personal computers and/or laptop computersrunning various versions of Microsoft Windows®, Apple Macintosh®, and/orLinux operating systems. The client computing devices can be workstationcomputers running any of a variety of commercially-available UNIX® orUNIX-like operating systems, including without limitation the variety ofGNU/Linux operating systems, such as for example, Google Chrome OS.Alternatively, or in addition, client computing devices may be any otherelectronic device, such as a thin-client computer, an Internet-enabledgaming system (e.g., a Microsoft Xbox gaming console with or without aKinect® gesture input device), and/or a personal messaging device,capable of communicating over a network that can access the VCN 1206and/or the Internet.

The VCN 1206 can include a local peering gateway (LPG) 1210 that can becommunicatively coupled to a secure shell (SSH) VCN 1212 via an LPG 1210contained in the SSH VCN 1212. The SSH VCN 1212 can include an SSHsubnet 1214, and the SSH VCN 1212 can be communicatively coupled to acontrol plane VCN 1216 via the LPG 1210 contained in the control planeVCN 1216. Also, the SSH VCN 1212 can be communicatively coupled to adata plane VCN 1218 via an LPG 1210. The control plane VCN 1216 and thedata plane VCN 1218 can be contained in a service tenancy 1219 that canbe owned and/or operated by the IaaS provider.

The control plane VCN 1216 can include a control plane demilitarizedzone (DMZ) tier 1220 that acts as a perimeter network (e.g., portions ofa corporate network between the corporate intranet and externalnetworks). The DMZ-based servers may have restricted responsibilitiesand help keep security breaches contained. Additionally, the DMZ tier1220 can include one or more load balancer (LB) subnet(s) 1222, acontrol plane app tier 1224 that can include app subnet(s) 1226, acontrol plane data tier 1228 that can include database (DB) subnet(s)1230 (e.g., frontend DB subnet(s) and/or backend DB subnet(s)). The LBsubnet(s) 1222 contained in the control plane DMZ tier 1220 can becommunicatively coupled to the app subnet(s) 1226 contained in thecontrol plane app tier 1224 and an Internet gateway 1234 that can becontained in the control plane VCN 1216, and the app subnet(s) 1226 canbe communicatively coupled to the DB subnet(s) 1230 contained in thecontrol plane data tier 1228 and a service gateway 1236 and a networkaddress translation (NAT) gateway 1238. The control plane VCN 1216 caninclude the service gateway 1236 and the NAT gateway 1238.

The control plane VCN 1216 can include a data plane mirror app tier 1240that can include app subnet(s) 1226. The app subnet(s) 1226 contained inthe data plane mirror app tier 1240 can include a virtual networkinterface controller (VNIC) 1242 that can execute a compute instance1244. The compute instance 1244 can communicatively couple the appsubnet(s) 1226 of the data plane mirror app tier 1240 to app subnet(s)1226 that can be contained in a data plane app tier 1246.

The data plane VCN 1218 can include the data plane app tier 1246, a dataplane DMZ tier 1248, and a data plane data tier 1250. The data plane DMZtier 1248 can include LB subnet(s) 1222 that can be communicativelycoupled to the app subnet(s) 1226 of the data plane app tier 1246 andthe Internet gateway 1234 of the data plane VCN 1218. The app subnet(s)1226 can be communicatively coupled to the service gateway 1236 of thedata plane VCN 1218 and the NAT gateway 1238 of the data plane VCN 1218.The data plane data tier 1250 can also include the DB subnet(s) 1230that can be communicatively coupled to the app subnet(s) 1226 of thedata plane app tier 1246.

The Internet gateway 1234 of the control plane VCN 1216 and of the dataplane VCN 1218 can be communicatively coupled to a metadata managementservice 1252 that can be communicatively coupled to public Internet1254. Public Internet 1254 can be communicatively coupled to the NATgateway 1238 of the control plane VCN 1216 and of the data plane VCN1218. The service gateway 1236 of the control plane VCN 1216 and of thedata plane VCN 1218 can be communicatively couple to cloud services1256.

In some examples, the service gateway 1236 of the control plane VCN 1216or of the data plan VCN 1218 can make application programming interface(API) calls to cloud services 1256 without going through public Internet1254. The API calls to cloud services 1256 from the service gateway 1236can be one-way: the service gateway 1236 can make API calls to cloudservices 1256, and cloud services 1256 can send requested data to theservice gateway 1236. But, cloud services 1256 may not initiate APIcalls to the service gateway 1236.

In some examples, the secure host tenancy 1204 can be directly connectedto the service tenancy 1219, which may be otherwise isolated. The securehost subnet 1208 can communicate with the SSH subnet 1214 through an LPG1210 that may enable two-way communication over an otherwise isolatedsystem. Connecting the secure host subnet 1208 to the SSH subnet 1214may give the secure host subnet 1208 access to other entities within theservice tenancy 1219.

The control plane VCN 1216 may allow users of the service tenancy 1219to set up or otherwise provision desired resources. Desired resourcesprovisioned in the control plane VCN 1216 may be deployed or otherwiseused in the data plane VCN 1218. In some examples, the control plane VCN1216 can be isolated from the data plane VCN 1218, and the data planemirror app tier 1240 of the control plane VCN 1216 can communicate withthe data plane app tier 1246 of the data plane VCN 1218 via VNICs 1242that can be contained in the data plane mirror app tier 1240 and thedata plane app tier 1246.

In some examples, users of the system, or customers, can make requests,for example create, read, update, or delete (CRUD) operations, throughpublic Internet 1254 that can communicate the requests to the metadatamanagement service 1252. The metadata management service 1252 cancommunicate the request to the control plane VCN 1216 through theInternet gateway 1234. The request can be received by the LB subnet(s)1222 contained in the control plane DMZ tier 1220. The LB subnet(s) 1222may determine that the request is valid, and in response to thisdetermination, the LB subnet(s) 1222 can transmit the request to appsubnet(s) 1226 contained in the control plane app tier 1224. If therequest is validated and requires a call to public Internet 1254, thecall to public Internet 1254 may be transmitted to the NAT gateway 1238that can make the call to public Internet 1254. Memory that may bedesired to be stored by the request can be stored in the DB subnet(s)1230.

In some examples, the data plane mirror app tier 1240 can facilitatedirect communication between the control plane VCN 1216 and the dataplane VCN 1218. For example, changes, updates, or other suitablemodifications to configuration may be desired to be applied to theresources contained in the data plane VCN 1218. Via a VNIC 1242, thecontrol plane VCN 1216 can directly communicate with, and can therebyexecute the changes, updates, or other suitable modifications toconfiguration to, resources contained in the data plane VCN 1218.

In some embodiments, the control plane VCN 1216 and the data plane VCN1218 can be contained in the service tenancy 1219. In this case, theuser, or the customer, of the system may not own or operate either thecontrol plane VCN 1216 or the data plane VCN 1218. Instead, the IaaSprovider may own or operate the control plane VCN 1216 and the dataplane VCN 1218, both of which may be contained in the service tenancy1219. This embodiment can enable isolation of networks that may preventusers or customers from interacting with other users', or othercustomers', resources. Also, this embodiment may allow users orcustomers of the system to store databases privately without needing torely on public Internet 1254, which may not have a desired level ofsecurity, for storage.

In other embodiments, the LB subnet(s) 1222 contained in the controlplane VCN 1216 can be configured to receive a signal from the servicegateway 1236. In this embodiment, the control plane VCN 1216 and thedata plane VCN 1218 may be configured to be called by a customer of theIaaS provider without calling public Internet 1254. Customers of theIaaS provider may desire this embodiment since database(s) that thecustomers use may be controlled by the IaaS provider and may be storedon the service tenancy 1219, which may be isolated from public Internet1254.

FIG. 13 is a block diagram 1300 illustrating another example pattern ofan IaaS architecture, according to at least one embodiment. Serviceoperators 1302 (e.g. service operators 1202 of FIG. 12) can becommunicatively coupled to a secure host tenancy 1304 (e.g. the securehost tenancy 1204 of FIG. 12) that can include a virtual cloud network(VCN) 1306 (e.g. the VCN 1206 of FIG. 12) and a secure host subnet 1308(e.g. the secure host subnet 1208 of FIG. 12). The VCN 1306 can includea local peering gateway (LPG) 1310 (e.g. the LPG 1210 of FIG. 12) thatcan be communicatively coupled to a secure shell (SSH) VCN 1312 (e.g.the SSH VCN 1212 of FIG. 12) via an LPG 1210 contained in the SSH VCN1312. The SSH VCN 1312 can include an SSH subnet 1314 (e.g. the SSHsubnet 1214 of FIG. 12), and the SSH VCN 1312 can be communicativelycoupled to a control plane VCN 1316 (e.g. the control plane VCN 1216 ofFIG. 12) via an LPG 1310 contained in the control plane VCN 1316. Thecontrol plane VCN 1316 can be contained in a service tenancy 1319 (e.g.the service tenancy 1219 of FIG. 12), and the data plane VCN 1318 (e.g.the data plane VCN 1218 of FIG. 12) can be contained in a customertenancy 1321 that may be owned or operated by users, or customers, ofthe system.

The control plane VCN 1316 can include a control plane DMZ tier 1320(e.g. the control plane DMZ tier 1220 of FIG. 12) that can include LBsubnet(s) 1322 (e.g. LB subnet(s) 1222 of FIG. 12), a control plane apptier 1324 (e.g. the control plane app tier 1224 of FIG. 12) that caninclude app subnet(s) 1326 (e.g. app subnet(s) 1226 of FIG. 12), acontrol plane data tier 1328 (e.g. the control plane data tier 1228 ofFIG. 12) that can include database (DB) subnet(s) 1330 (e.g. similar toDB subnet(s) 1230 of FIG. 12). The LB subnet(s) 1322 contained in thecontrol plane DMZ tier 1320 can be communicatively coupled to the appsubnet(s) 1326 contained in the control plane app tier 1324 and anInternet gateway 1334 (e.g. the Internet gateway 1234 of FIG. 12) thatcan be contained in the control plane VCN 1316, and the app subnet(s)1326 can be communicatively coupled to the DB subnet(s) 1330 containedin the control plane data tier 1328 and a service gateway 1336 (e.g. theservice gateway of FIG. 12) and a network address translation (NAT)gateway 1338 (e.g. the NAT gateway 1238 of FIG. 12). The control planeVCN 1316 can include the service gateway 1336 and the NAT gateway 1338.

The control plane VCN 1316 can include a data plane mirror app tier 1340(e.g. the data plane mirror app tier 1240 of FIG. 12) that can includeapp subnet(s) 1326. The app subnet(s) 1326 contained in the data planemirror app tier 1340 can include a virtual network interface controller(VNIC) 1342 (e.g. the VNIC of 1242) that can execute a compute instance1344 (e.g. similar to the compute instance 1244 of FIG. 12). The computeinstance 1344 can facilitate communication between the app subnet(s)1326 of the data plane mirror app tier 1340 and the app subnet(s) 1326that can be contained in a data plane app tier 1346 (e.g. the data planeapp tier 1246 of FIG. 12) via the VNIC 1342 contained in the data planemirror app tier 1340 and the VNIC 1342 contained in the data plan apptier 1346.

The Internet gateway 1334 contained in the control plane VCN 1316 can becommunicatively coupled to a metadata management service 1352 (e.g. themetadata management service 1252 of FIG. 12) that can be communicativelycoupled to public Internet 1354 (e.g. public Internet 1254 of FIG. 12).Public Internet 1354 can be communicatively coupled to the NAT gateway1338 contained in the control plane VCN 1316. The service gateway 1336contained in the control plane VCN 1316 can be communicatively couple tocloud services 1356 (e.g. cloud services 1256 of FIG. 12).

In some examples, the data plane VCN 1318 can be contained in thecustomer tenancy 1321. In this case, the IaaS provider may provide thecontrol plane VCN 1316 for each customer, and the IaaS provider may, foreach customer, set up a unique compute instance 1344 that is containedin the service tenancy 1319. Each compute instance 1344 may allowcommunication between the control plane VCN 1316, contained in theservice tenancy 1319, and the data plane VCN 1318 that is contained inthe customer tenancy 1321. The compute instance 1344 may allowresources, that are provisioned in the control plane VCN 1316 that iscontained in the service tenancy 1319, to be deployed or otherwise usedin the data plane VCN 1318 that is contained in the customer tenancy1321.

In other examples, the customer of the IaaS provider may have databasesthat live in the customer tenancy 1321. In this example, the controlplane VCN 1316 can include the data plane mirror app tier 1340 that caninclude app subnet(s) 1326. The data plane mirror app tier 1340 canreside in the data plane VCN 1318, but the data plane mirror app tier1340 may not live in the data plane VCN 1318. That is, the data planemirror app tier 1340 may have access to the customer tenancy 1321, butthe data plane mirror app tier 1340 may not exist in the data plane VCN1318 or be owned or operated by the customer of the IaaS provider. Thedata plane mirror app tier 1340 may be configured to make calls to thedata plane VCN 1318 but may not be configured to make calls to anyentity contained in the control plane VCN 1316. The customer may desireto deploy or otherwise use resources in the data plane VCN 1318 that areprovisioned in the control plane VCN 1316, and the data plane mirror apptier 1340 can facilitate the desired deployment, or other usage ofresources, of the customer.

In some embodiments, the customer of the IaaS provider can apply filtersto the data plane VCN 1318. In this embodiment, the customer candetermine what the data plane VCN 1318 can access, and the customer mayrestrict access to public Internet 1354 from the data plane VCN 1318.The IaaS provider may not be able to apply filters or otherwise controlaccess of the data plane VCN 1318 to any outside networks or databases.Applying filters and controls by the customer onto the data plane VCN1318, contained in the customer tenancy 1321, can help isolate the dataplane VCN 1318 from other customers and from public Internet 1354.

In some embodiments, cloud services 1356 can be called by the servicegateway 1336 to access services that may not exist on public Internet1354, on the control plane VCN 1316, or on the data plane VCN 1318. Theconnection between cloud services 1356 and the control plane VCN 1316 orthe data plane VCN 1318 may not be live or continuous. Cloud services1356 may exist on a different network owned or operated by the IaaSprovider. Cloud services 1356 may be configured to receive calls fromthe service gateway 1336 and may be configured to not receive calls frompublic Internet 1354. Some cloud services 1356 may be isolated fromother cloud services 1356, and the control plane VCN 1316 may beisolated from cloud services 1356 that may not be in the same region asthe control plane VCN 1316. For example, the control plane VCN 1316 maybe located in “Region 1,” and cloud service “Deployment 12,” may belocated in Region 1 and in “Region 2.” If a call to Deployment 12 ismade by the service gateway 1336 contained in the control plane VCN 1316located in Region 1, the call may be transmitted to Deployment 12 inRegion 1. In this example, the control plane VCN 1316, or Deployment 12in Region 1, may not be communicatively coupled to, or otherwise incommunication with, Deployment 12 in Region 2.

FIG. 14 is a block diagram 1400 illustrating another example pattern ofan IaaS architecture, according to at least one embodiment. Serviceoperators 1402 (e.g. service operators 1202 of FIG. 12) can becommunicatively coupled to a secure host tenancy 1404 (e.g. the securehost tenancy 1204 of FIG. 12) that can include a virtual cloud network(VCN) 1406 (e.g. the VCN 1206 of FIG. 12) and a secure host subnet 1408(e.g. the secure host subnet 1208 of FIG. 12). The VCN 1406 can includean LPG 1410 (e.g. the LPG 1210 of FIG. 12) that can be communicativelycoupled to an SSH VCN 1412 (e.g. the SSH VCN 1212 of FIG. 12) via an LPG1410 contained in the SSH VCN 1412. The SSH VCN 1412 can include an SSHsubnet 1414 (e.g. the SSH subnet 1214 of FIG. 12), and the SSH VCN 1412can be communicatively coupled to a control plane VCN 1416 (e.g. thecontrol plane VCN 1216 of FIG. 12) via an LPG 1410 contained in thecontrol plane VCN 1416 and to a data plane VCN 1418 (e.g. the data plane1218 of FIG. 12) via an LPG 1410 contained in the data plane VCN 1418.The control plane VCN 1416 and the data plane VCN 1418 can be containedin a service tenancy 1419 (e.g. the service tenancy 1219 of FIG. 12).

The control plane VCN 1416 can include a control plane DMZ tier 1420(e.g. the control plane DMZ tier 1220 of FIG. 12) that can include loadbalancer (LB) subnet(s) 1422 (e.g. LB subnet(s) 1222 of FIG. 12), acontrol plane app tier 1424 (e.g. the control plane app tier 1224 ofFIG. 12) that can include app subnet(s) 1426 (e.g. similar to appsubnet(s) 1226 of FIG. 12), a control plane data tier 1428 (e.g. thecontrol plane data tier 1228 of FIG. 12) that can include DB subnet(s)1430. The LB subnet(s) 1422 contained in the control plane DMZ tier 1420can be communicatively coupled to the app subnet(s) 1426 contained inthe control plane app tier 1424 and to an Internet gateway 1434 (e.g.the Internet gateway 1234 of FIG. 12) that can be contained in thecontrol plane VCN 1416, and the app subnet(s) 1426 can becommunicatively coupled to the DB subnet(s) 1430 contained in thecontrol plane data tier 1428 and to a service gateway 1436 (e.g. theservice gateway of FIG. 12) and a network address translation (NAT)gateway 1438 (e.g. the NAT gateway 1238 of FIG. 12). The control planeVCN 1416 can include the service gateway 1436 and the NAT gateway 1438.

The data plane VCN 1418 can include a data plane app tier 1446 (e.g. thedata plane app tier 1246 of FIG. 12), a data plane DMZ tier 1448 (e.g.the data plane DMZ tier 1248 of FIG. 12), and a data plane data tier1450 (e.g. the data plane data tier 1250 of FIG. 12). The data plane DMZtier 1448 can include LB subnet(s) 1422 that can be communicativelycoupled to trusted app subnet(s) 1460 and untrusted app subnet(s) 1462of the data plane app tier 1446 and the Internet gateway 1434 containedin the data plane VCN 1418. The trusted app subnet(s) 1460 can becommunicatively coupled to the service gateway 1436 contained in thedata plane VCN 1418, the NAT gateway 1438 contained in the data planeVCN 1418, and DB subnet(s) 1430 contained in the data plane data tier1450. The untrusted app subnet(s) 1462 can be communicatively coupled tothe service gateway 1436 contained in the data plane VCN 1418 and DBsubnet(s) 1430 contained in the data plane data tier 1450. The dataplane data tier 1450 can include DB subnet(s) 1430 that can becommunicatively coupled to the service gateway 1436 contained in thedata plane VCN 1418.

The untrusted app subnet(s) 1462 can include one or more primary VNICs1464(1)-(N) that can be communicatively coupled to tenant virtualmachines (VMs) 1466(1)-(N). Each tenant VM 1466(1)-(N) can becommunicatively coupled to a respective app subnet 1467(1)-(N) that canbe contained in respective container egress VCNs 1468(1)-(N) that can becontained in respective customer tenancies 1470(1)-(N). Respectivesecondary VNICs 1472(1)-(N) can facilitate communication between theuntrusted app subnet(s) 1462 contained in the data plane VCN 1418 andthe app subnet contained in the container egress VCNs 1468(1)-(N). Eachcontainer egress VCNs 1468(1)-(N) can include a NAT gateway 1438 thatcan be communicatively coupled to public Internet 1454 (e.g. publicInternet 1254 of FIG. 12).

The Internet gateway 1434 contained in the control plane VCN 1416 andcontained in the data plane VCN 1418 can be communicatively coupled to ametadata management service 1452 (e.g. the metadata management system1252 of FIG. 12) that can be communicatively coupled to public Internet1454. Public Internet 1454 can be communicatively coupled to the NATgateway 1438 contained in the control plane VCN 1416 and contained inthe data plane VCN 1418. The service gateway 1436 contained in thecontrol plane VCN 1416 and contained in the data plane VCN 1418 can becommunicatively couple to cloud services 1456.

In some embodiments, the data plane VCN 1418 can be integrated withcustomer tenancies 1470. This integration can be useful or desirable forcustomers of the IaaS provider in some cases such as a case that maydesire support when executing code. The customer may provide code to runthat may be destructive, may communicate with other customer resources,or may otherwise cause undesirable effects. In response to this, theIaaS provider may determine whether to run code given to the IaaSprovider by the customer.

In some examples, the customer of the IaaS provider may grant temporarynetwork access to the IaaS provider and request a function to beattached to the data plane tier app 1446. Code to run the function maybe executed in the VMs 1466(1)-(N), and the code may not be configuredto run anywhere else on the data plane VCN 1418. Each VM 1466(1)-(N) maybe connected to one customer tenancy 1470. Respective containers1471(1)-(N) contained in the VMs 1466(1)-(N) may be configured to runthe code. In this case, there can be a dual isolation (e.g., thecontainers 1471(1)-(N) running code, where the containers 1471(1)-(N)may be contained in at least the VM 1466(1)-(N) that are contained inthe untrusted app subnet(s) 1462), which may help prevent incorrect orotherwise undesirable code from damaging the network of the IaaSprovider or from damaging a network of a different customer. Thecontainers 1471(1)-(N) may be communicatively coupled to the customertenancy 1470 and may be configured to transmit or receive data from thecustomer tenancy 1470. The containers 1471(1)-(N) may not be configuredto transmit or receive data from any other entity in the data plane VCN1418. Upon completion of running the code, the IaaS provider may kill orotherwise dispose of the containers 1471(1)-(N).

In some embodiments, the trusted app subnet(s) 1460 may run code thatmay be owned or operated by the IaaS provider. In this embodiment, thetrusted app subnet(s) 1460 may be communicatively coupled to the DBsubnet(s) 1430 and be configured to execute CRUD operations in the DBsubnet(s) 1430. The untrusted app subnet(s) 1462 may be communicativelycoupled to the DB subnet(s) 1430, but in this embodiment, the untrustedapp subnet(s) may be configured to execute read operations in the DBsubnet(s) 1430. The containers 1471(1)-(N) that can be contained in theVM 1466(1)-(N) of each customer and that may run code from the customermay not be communicatively coupled with the DB subnet(s) 1430.

In other embodiments, the control plane VCN 1416 and the data plane VCN1418 may not be directly communicatively coupled. In this embodiment,there may be no direct communication between the control plane VCN 1416and the data plane VCN 1418. However, communication can occur indirectlythrough at least one method. An LPG 1410 may be established by the IaaSprovider that can facilitate communication between the control plane VCN1416 and the data plane VCN 1418. In another example, the control planeVCN 1416 or the data plane VCN 1418 can make a call to cloud services1456 via the service gateway 1436. For example, a call to cloud services1456 from the control plane VCN 1416 can include a request for a servicethat can communicate with the data plane VCN 1418.

FIG. 15 is a block diagram 1500 illustrating another example pattern ofan IaaS architecture, according to at least one embodiment. Serviceoperators 1502 (e.g. service operators 1202 of FIG. 12) can becommunicatively coupled to a secure host tenancy 1504 (e.g. the securehost tenancy 1204 of FIG. 12) that can include a virtual cloud network(VCN) 1506 (e.g. the VCN 1206 of FIG. 12) and a secure host subnet 1508(e.g. the secure host subnet 1208 of FIG. 12). The VCN 1506 can includean LPG 1510 (e.g. the LPG 1210 of FIG. 12) that can be communicativelycoupled to an SSH VCN 1512 (e.g. the SSH VCN 1212 of FIG. 12) via an LPG1510 contained in the SSH VCN 1512. The SSH VCN 1512 can include an SSHsubnet 1514 (e.g. the SSH subnet 1214 of FIG. 12), and the SSH VCN 1512can be communicatively coupled to a control plane VCN 1516 (e.g. thecontrol plane VCN 1216 of FIG. 12) via an LPG 1510 contained in thecontrol plane VCN 1516 and to a data plane VCN 1518 (e.g. the data plane1218 of FIG. 12) via an LPG 1510 contained in the data plane VCN 1518.The control plane VCN 1516 and the data plane VCN 1518 can be containedin a service tenancy 1519 (e.g. the service tenancy 1219 of FIG. 12).

The control plane VCN 1516 can include a control plane DMZ tier 1520(e.g. the control plane DMZ tier 1220 of FIG. 12) that can include LBsubnet(s) 1522 (e.g. LB subnet(s) 1222 of FIG. 12), a control plane apptier 1524 (e.g. the control plane app tier 1224 of FIG. 12) that caninclude app subnet(s) 1526 (e.g. app subnet(s) 1226 of FIG. 12), acontrol plane data tier 1528 (e.g. the control plane data tier 1228 ofFIG. 12) that can include DB subnet(s) 1530 (e.g. DB subnet(s) 1430 ofFIG. 14). The LB subnet(s) 1522 contained in the control plane DMZ tier1520 can be communicatively coupled to the app subnet(s) 1526 containedin the control plane app tier 1524 and to an Internet gateway 1534 (e.g.the Internet gateway 1234 of FIG. 12) that can be contained in thecontrol plane VCN 1516, and the app subnet(s) 1526 can becommunicatively coupled to the DB subnet(s) 1530 contained in thecontrol plane data tier 1528 and to a service gateway 1536 (e.g. theservice gateway of FIG. 12) and a network address translation (NAT)gateway 1538 (e.g. the NAT gateway 1238 of FIG. 12). The control planeVCN 1516 can include the service gateway 1536 and the NAT gateway 1538.

The data plane VCN 1518 can include a data plane app tier 1546 (e.g. thedata plane app tier 1246 of FIG. 12), a data plane DMZ tier 1548 (e.g.the data plane DMZ tier 1248 of FIG. 12), and a data plane data tier1550 (e.g. the data plane data tier 1250 of FIG. 12). The data plane DMZtier 1548 can include LB subnet(s) 1522 that can be communicativelycoupled to trusted app subnet(s) 1560 (e.g. trusted app subnet(s) 1460of FIG. 14) and untrusted app subnet(s) 1562 (e.g. untrusted appsubnet(s) 1462 of FIG. 14) of the data plane app tier 1546 and theInternet gateway 1534 contained in the data plane VCN 1518. The trustedapp subnet(s) 1560 can be communicatively coupled to the service gateway1536 contained in the data plane VCN 1518, the NAT gateway 1538contained in the data plane VCN 1518, and DB subnet(s) 1530 contained inthe data plane data tier 1550. The untrusted app subnet(s) 1562 can becommunicatively coupled to the service gateway 1536 contained in thedata plane VCN 1518 and DB subnet(s) 1530 contained in the data planedata tier 1550. The data plane data tier 1550 can include DB subnet(s)1530 that can be communicatively coupled to the service gateway 1536contained in the data plane VCN 1518.

The untrusted app subnet(s) 1562 can include primary VNICs 1564(1)-(N)that can be communicatively coupled to tenant virtual machines (VMs)1566(1)-(N) residing within the untrusted app subnet(s) 1562. Eachtenant VM 1566(1)-(N) can run code in a respective container1567(1)-(N), and be communicatively coupled to an app subnet 1526 thatcan be contained in a data plane app tier 1546 that can be contained ina container egress VCN 1568. Respective secondary VNICs 1572(1)-(N) canfacilitate communication between the untrusted app subnet(s) 1562contained in the data plane VCN 1518 and the app subnet contained in thecontainer egress VCN 1568. The container egress VCN can include a NATgateway 1538 that can be communicatively coupled to public Internet 1554(e.g. public Internet 1254 of FIG. 12).

The Internet gateway 1534 contained in the control plane VCN 1516 andcontained in the data plane VCN 1518 can be communicatively coupled to ametadata management service 1552 (e.g. the metadata management system1252 of FIG. 12) that can be communicatively coupled to public Internet1554. Public Internet 1554 can be communicatively coupled to the NATgateway 1538 contained in the control plane VCN 1516 and contained inthe data plane VCN 1518. The service gateway 1536 contained in thecontrol plane VCN 1516 and contained in the data plane VCN 1518 can becommunicatively couple to cloud services 1556.

In some examples, the pattern illustrated by the architecture of blockdiagram 1500 of FIG. 15 may be considered an exception to the patternillustrated by the architecture of block diagram 1400 of FIG. 14 and maybe desirable for a customer of the IaaS provider if the IaaS providercannot directly communicate with the customer (e.g., a disconnectedregion). The respective containers 1567(1)-(N) that are contained in theVMs 1566(1)-(N) for each customer can be accessed in real-time by thecustomer. The containers 1567(1)-(N) may be configured to make calls torespective secondary VNICs 1572(1)-(N) contained in app subnet(s) 1526of the data plane app tier 1546 that can be contained in the containeregress VCN 1568. The secondary VNICs 1572(1)-(N) can transmit the callsto the NAT gateway 1538 that may transmit the calls to public Internet1554. In this example, the containers 1567(1)-(N) that can be accessedin real-time by the customer can be isolated from the control plane VCN1516 and can be isolated from other entities contained in the data planeVCN 1518. The containers 1567(1)-(N) may also be isolated from resourcesfrom other customers.

In other examples, the customer can use the containers 1567(1)-(N) tocall cloud services 1556. In this example, the customer may run code inthe containers 1567(1)-(N) that requests a service from cloud services1556. The containers 1567(1)-(N) can transmit this request to thesecondary VNICs 1572(1)-(N) that can transmit the request to the NATgateway that can transmit the request to public Internet 1554. PublicInternet 1554 can transmit the request to LB subnet(s) 1522 contained inthe control plane VCN 1516 via the Internet gateway 1534. In response todetermining the request is valid, the LB subnet(s) can transmit therequest to app subnet(s) 1526 that can transmit the request to cloudservices 1556 via the service gateway 1536.

It should be appreciated that IaaS architectures 1200, 1300, 1400, 1500depicted in the figures may have other components than those depicted.Further, the embodiments shown in the figures are only some examples ofa cloud infrastructure system that may incorporate an embodiment of thedisclosure. In some other embodiments, the IaaS systems may have more orfewer components than shown in the figures, may combine two or morecomponents, or may have a different configuration or arrangement ofcomponents.

In certain embodiments, the IaaS systems described herein may include asuite of applications, middleware, and database service offerings thatare delivered to a customer in a self-service, subscription-based,elastically scalable, reliable, highly available, and secure manner. Anexample of such an IaaS system is the Oracle Cloud Infrastructure (OCI)provided by the present assignee.

FIG. 16 illustrates an example computer system 1600, in which variousembodiments of the present disclosure may be implemented. The system1600 may be used to implement any of the computer systems describedabove. As shown in the figure, computer system 1600 includes aprocessing unit 1604 that communicates with a number of peripheralsubsystems via a bus subsystem 1602. These peripheral subsystems mayinclude a processing acceleration unit 1606, an I/O subsystem 1608, astorage subsystem 1618 and a communications subsystem 1624. Storagesubsystem 1618 includes tangible computer-readable storage media 1622and a system memory 1610.

Bus subsystem 1602 provides a mechanism for letting the variouscomponents and subsystems of computer system 1600 communicate with eachother as intended. Although bus subsystem 1602 is shown schematically asa single bus, alternative embodiments of the bus subsystem may utilizemultiple buses. Bus subsystem 1602 may be any of several types of busstructures including a memory bus or memory controller, a peripheralbus, and a local bus using any of a variety of bus architectures. Forexample, such architectures may include an Industry StandardArchitecture (ISA) bus, Micro Channel Architecture (MCA) bus, EnhancedISA (EISA) bus, Video Electronics Standards Association (VESA) localbus, and Peripheral Component Interconnect (PCI) bus, which can beimplemented as a Mezzanine bus manufactured to the IEEE P1386.1standard.

Processing unit 1604, which can be implemented as one or more integratedcircuits (e.g., a conventional microprocessor or microcontroller),controls the operation of computer system 1600. One or more processorsmay be included in processing unit 1604. These processors may includesingle core or multicore processors. In certain embodiments, processingunit 1604 may be implemented as one or more independent processing units1632 and/or 1634 with single or multicore processors included in eachprocessing unit. In other embodiments, processing unit 1604 may also beimplemented as a quad-core processing unit formed by integrating twodual-core processors into a single chip.

In various embodiments, processing unit 1604 can execute a variety ofprograms in response to program code and can maintain multipleconcurrently executing programs or processes. At any given time, some orall of the program code to be executed can be resident in processor(s)1604 and/or in storage subsystem 1618. Through suitable programming,processor(s) 1604 can provide various functionalities described above.Computer system 1600 may additionally include a processing accelerationunit 1606, which can include a digital signal processor (DSP), aspecial-purpose processor, and/or the like.

I/O subsystem 1608 may include user interface input devices and userinterface output devices. User interface input devices may include akeyboard, pointing devices such as a mouse or trackball, a touchpad ortouch screen incorporated into a display, a scroll wheel, a click wheel,a dial, a button, a switch, a keypad, audio input devices with voicecommand recognition systems, microphones, and other types of inputdevices. User interface input devices may include, for example, motionsensing and/or gesture recognition devices such as the Microsoft Kinect®motion sensor that enables users to control and interact with an inputdevice, such as the Microsoft Xbox® 360 game controller, through anatural user interface using gestures and spoken commands. Userinterface input devices may also include eye gesture recognition devicessuch as the Google Glass® blink detector that detects eye activity(e.g., ‘blinking’ while taking pictures and/or making a menu selection)from users and transforms the eye gestures as input into an input device(e.g., Google Glass®). Additionally, user interface input devices mayinclude voice recognition sensing devices that enable users to interactwith voice recognition systems (e.g., Siri® navigator), through voicecommands.

User interface input devices may also include, without limitation, threedimensional (3D) mice, joysticks or pointing sticks, gamepads andgraphic tablets, and audio/visual devices such as speakers, digitalcameras, digital camcorders, portable media players, webcams, imagescanners, fingerprint scanners, barcode reader 3D scanners, 3D printers,laser rangefinders, and eye gaze tracking devices. Additionally, userinterface input devices may include, for example, medical imaging inputdevices such as computed tomography, magnetic resonance imaging,position emission tomography, medical ultrasonography devices. Userinterface input devices may also include, for example, audio inputdevices such as MIDI keyboards, digital musical instruments and thelike.

User interface output devices may include a display subsystem, indicatorlights, or non-visual displays such as audio output devices, etc. Thedisplay subsystem may be a cathode ray tube (CRT), a flat-panel device,such as that using a liquid crystal display (LCD) or plasma display, aprojection device, a touch screen, and the like. In general, use of theterm “output device” is intended to include all possible types ofdevices and mechanisms for outputting information from computer system1600 to a user or other computer. For example, user interface outputdevices may include, without limitation, a variety of display devicesthat visually convey text, graphics and audio/video information such asmonitors, printers, speakers, headphones, automotive navigation systems,plotters, voice output devices, and modems.

Computer system 1600 may comprise a storage subsystem 1618 thatcomprises software elements, shown as being currently located within asystem memory 1610. System memory 1610 may store program instructionsthat are loadable and executable on processing unit 1604, as well asdata generated during the execution of these programs.

Depending on the configuration and type of computer system 1600, systemmemory 1610 may be volatile (such as random access memory (RAM)) and/ornon-volatile (such as read-only memory (ROM), flash memory, etc.) TheRAM typically contains data and/or program modules that are immediatelyaccessible to and/or presently being operated and executed by processingunit 1604. In some implementations, system memory 1610 may includemultiple different types of memory, such as static random access memory(SRAM) or dynamic random access memory (DRAM). In some implementations,a basic input/output system (BIOS), containing the basic routines thathelp to transfer information between elements within computer system1600, such as during start-up, may typically be stored in the ROM. Byway of example, and not limitation, system memory 1610 also illustratesapplication programs 1612, which may include client applications, Webbrowsers, mid-tier applications, relational database management systems(RDBMS), etc., program data 1614, and an operating system 1616. By wayof example, operating system 1616 may include various versions ofMicrosoft Windows®, Apple Macintosh®, and/or Linux operating systems, avariety of commercially-available UNIX® or UNIX-like operating systems(including without limitation the variety of GNU/Linux operatingsystems, the Google Chrome® OS, and the like) and/or mobile operatingsystems such as iOS, Windows® Phone, Android® OS, BlackBerry® 16 OS, andPalm® OS operating systems.

Storage subsystem 1618 may also provide a tangible computer-readablestorage medium for storing the basic programming and data constructsthat provide the functionality of some embodiments. Software (programs,code modules, instructions) that when executed by a processor providethe functionality described above may be stored in storage subsystem1618. These software modules or instructions may be executed byprocessing unit 1604. Storage subsystem 1618 may also provide arepository for storing data used in accordance with the presentdisclosure.

Storage subsystem 1600 may also include a computer-readable storagemedia reader 1620 that can further be connected to computer-readablestorage media 1622. Together and, optionally, in combination with systemmemory 1610, computer-readable storage media 1622 may comprehensivelyrepresent remote, local, fixed, and/or removable storage devices plusstorage media for temporarily and/or more permanently containing,storing, transmitting, and retrieving computer-readable information.

Computer-readable storage media 1622 containing code, or portions ofcode, can also include any appropriate media known or used in the art,including storage media and communication media, such as but not limitedto, volatile and non-volatile, removable and non-removable mediaimplemented in any method or technology for storage and/or transmissionof information. This can include tangible computer-readable storagemedia such as RAM, ROM, electronically erasable programmable ROM(EEPROM), flash memory or other memory technology, CD-ROM, digitalversatile disk (DVD), or other optical storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or other tangible computer readable media. This can also includenontangible computer-readable media, such as data signals, datatransmissions, or any other medium which can be used to transmit thedesired information and which can be accessed by computing system 1600.

By way of example, computer-readable storage media 1622 may include ahard disk drive that reads from or writes to non-removable, nonvolatilemagnetic media, a magnetic disk drive that reads from or writes to aremovable, nonvolatile magnetic disk, and an optical disk drive thatreads from or writes to a removable, nonvolatile optical disk such as aCD ROM, DVD, and Blu-Ray® disk, or other optical media.Computer-readable storage media 1622 may include, but is not limited to,Zip® drives, flash memory cards, universal serial bus (USB) flashdrives, secure digital (SD) cards, DVD disks, digital video tape, andthe like. Computer-readable storage media 1622 may also include,solid-state drives (SSD) based on non-volatile memory such asflash-memory based SSDs, enterprise flash drives, solid state ROM, andthe like, SSDs based on volatile memory such as solid state RAM, dynamicRAM, static RAM, DRAM-based SSDs, magnetoresistive RAM (MRAM) SSDs, andhybrid SSDs that use a combination of DRAM and flash memory based SSDs.The disk drives and their associated computer-readable media may providenon-volatile storage of computer-readable instructions, data structures,program modules, and other data for computer system 1600.

Communications subsystem 1624 provides an interface to other computersystems and networks. Communications subsystem 1624 serves as aninterface for receiving data from and transmitting data to other systemsfrom computer system 1600. For example, communications subsystem 1624may enable computer system 1600 to connect to one or more devices viathe Internet. In some embodiments communications subsystem 1624 caninclude radio frequency (RF) transceiver components for accessingwireless voice and/or data networks (e.g., using cellular telephonetechnology, advanced data network technology, such as 3G, 4G or EDGE(enhanced data rates for global evolution), WiFi (IEEE 802.11 familystandards, or other mobile communication technologies, or anycombination thereof), global positioning system (GPS) receivercomponents, and/or other components. In some embodiments communicationssubsystem 1624 can provide wired network connectivity (e.g., Ethernet)in addition to or instead of a wireless interface.

In some embodiments, communications subsystem 1624 may also receiveinput communication in the form of structured and/or unstructured datafeeds 1626, event streams 1628, event updates 1630, and the like onbehalf of one or more users who may use computer system 1600.

By way of example, communications subsystem 1624 may be configured toreceive data feeds 1626 in real-time from users of social networksand/or other communication services such as Twitter® feeds, Facebook®updates, web feeds such as Rich Site Summary (RSS) feeds, and/orreal-time updates from one or more third party information sources.

Additionally, communications subsystem 1624 may also be configured toreceive data in the form of continuous data streams, which may includeevent streams 1628 of real-time events and/or event updates 1630, thatmay be continuous or unbounded in nature with no explicit end. Examplesof applications that generate continuous data may include, for example,sensor data applications, financial tickers, network performancemeasuring tools (e.g. network monitoring and traffic managementapplications), clickstream analysis tools, automobile trafficmonitoring, and the like.

Communications subsystem 1624 may also be configured to output thestructured and/or unstructured data feeds 1626, event streams 1628,event updates 1630, and the like to one or more databases that may be incommunication with one or more streaming data source computers coupledto computer system 1600.

Computer system 1600 can be one of various types, including a handheldportable device (e.g., an iPhone® cellular phone, an iPad® computingtablet, a PDA), a wearable device (e.g., a Google Glass® head mounteddisplay), a PC, a workstation, a mainframe, a kiosk, a server rack, orany other data processing system.

Due to the ever-changing nature of computers and networks, thedescription of computer system 1600 depicted in the figure is intendedonly as a specific example. Many other configurations having more orfewer components than the system depicted in the figure are possible.For example, customized hardware might also be used and/or particularelements might be implemented in hardware, firmware, software (includingapplets), or a combination. Further, connection to other computingdevices, such as network input/output devices, may be employed. Based onthe disclosure and teachings provided herein, a person of ordinary skillin the art will appreciate other ways and/or methods to implement thevarious embodiments.

Although specific embodiments of the disclosure have been described,various modifications, alterations, alternative constructions, andequivalents are also encompassed within the scope of the disclosure.Embodiments of the present disclosure are not restricted to operationwithin certain specific data processing environments, but are free tooperate within a plurality of data processing environments.Additionally, although embodiments of the present disclosure have beendescribed using a particular series of transactions and steps, it shouldbe apparent to those skilled in the art that the scope of the presentdisclosure is not limited to the described series of transactions andsteps. Various features and aspects of the above-described embodimentsmay be used individually or jointly.

Further, while embodiments of the present disclosure have been describedusing a particular combination of hardware and software, it should berecognized that other combinations of hardware and software are alsowithin the scope of the present disclosure. Embodiments of the presentdisclosure may be implemented only in hardware, or only in software, orusing combinations thereof. The various processes described herein canbe implemented on the same processor or different processors in anycombination. Accordingly, where components or modules are described asbeing configured to perform certain operations, such configuration canbe accomplished, e.g., by designing electronic circuits to perform theoperation, by programming programmable electronic circuits (such asmicroprocessors) to perform the operation, or any combination thereof.Processes can communicate using a variety of techniques including butnot limited to conventional techniques for inter process communication,and different pairs of processes may use different techniques, or thesame pair of processes may use different techniques at different times.

The specification and drawings are, accordingly, to be regarded in anillustrative rather than a restrictive sense. It will, however, beevident that additions, subtractions, deletions, and other modificationsand changes may be made thereunto without departing from the broaderspirit and scope as set forth in the claims. Thus, although specificdisclosure embodiments have been described, these are not intended to belimiting. Various modifications and equivalents are within the scope ofthe following claims.

The use of the terms “a” and “an” and “the” and similar referents in thecontext of describing the disclosed embodiments (especially in thecontext of the following claims) are to be construed to cover both thesingular and the plural, unless otherwise indicated herein or clearlycontradicted by context. The terms “comprising,” “having,” “including,”and “containing” are to be construed as open-ended terms (i.e., meaning“including, but not limited to,”) unless otherwise noted. The term“connected” is to be construed as partly or wholly contained within,attached to, or joined together, even if there is something intervening.Recitation of ranges of values herein are merely intended to serve as ashorthand method of referring individually to each separate valuefalling within the range, unless otherwise indicated herein and eachseparate value is incorporated into the specification as if it wereindividually recited herein. All methods described herein can beperformed in any suitable order unless otherwise indicated herein orotherwise clearly contradicted by context. The use of any and allexamples, or exemplary language (e.g., “such as”) provided herein, isintended merely to better illuminate embodiments of the disclosure anddoes not pose a limitation on the scope of the disclosure unlessotherwise claimed. No language in the specification should be construedas indicating any non-claimed element as essential to the practice ofthe disclosure.

Disjunctive language such as the phrase “at least one of X, Y, or Z,”unless specifically stated otherwise, is intended to be understoodwithin the context as used in general to present that an item, term,etc., may be either X, Y, or Z, or any combination thereof (e.g., X, Y,and/or Z). Thus, such disjunctive language is not generally intended to,and should not, imply that certain embodiments require at least one ofX, at least one of Y, or at least one of Z to each be present.

Preferred embodiments of this disclosure are described herein, includingthe best mode known to the inventors for carrying out the disclosure.Variations of those preferred embodiments may become apparent to thoseof ordinary skill in the art upon reading the foregoing description. Theinventors expect skilled artisans to employ such variations asappropriate and the inventors intend for the disclosure to be practicedotherwise than as specifically described herein. Accordingly, thisdisclosure includes all modifications and equivalents of the subjectmatter recited in the claims appended hereto as permitted by applicablelaw. Moreover, any combination of the above-described elements in allpossible variations thereof is encompassed by the disclosure unlessotherwise indicated herein or otherwise clearly contradicted by context.

All references, including publications, patent applications, andpatents, cited herein are hereby incorporated by reference to the sameextent as if each reference were individually and specifically indicatedto be incorporated by reference and were set forth in its entiretyherein.

In the foregoing specification, aspects of the disclosure are describedwith reference to specific embodiments thereof, but those skilled in theart will recognize that the disclosure is not limited thereto. Variousfeatures and aspects of the above-described disclosure may be usedindividually or jointly. Further, embodiments can be utilized in anynumber of environments and applications beyond those described hereinwithout departing from the broader spirit and scope of thespecification. The specification and drawings are, accordingly, to beregarded as illustrative rather than restrictive.

What is claimed is:
 1. A computer-implemented method, comprising:determining, by a software service, a set of computing instances of adistributed computing environment to be probed by a plurality of healthassessment applications, the set of computing instances being determinedfrom a plurality of computing instances based at least in part on anestimated resource consumption for obtaining respective healthassessment data corresponding to the set of computing instances and anavailable compute resource capacity of a computing component hosting aparticular health assessment application; obtaining, by the softwareservice from a health assessment application of the plurality of healthassessment applications, health assessment data corresponding to acomputing instance of the set of computing instances; receiving arequest for first collective health assessment data of a subset ofcomputing instances of the set of computing instances, the subset ofcomputing instances being associated with a client; obtaining, from afirst distributed cache, the first collective health assessment datacorresponding to the subset of computing instances associated with theclient; providing the first collective health assessment data for thesubset of computing instances in response to the request; storing thefirst collective health assessment data in a second distributed cache asstored health assessment data; receiving a subsequent request for secondcollective health assessment data for the subset of computing instancesassociated with the client; obtaining, from the first distributed cache,the second collective health assessment data corresponding to the subsetof computing instances; calculating change data indicative of adifference between the first collective health assessment data and thesecond collective health assessment data; providing the change data inresponse to the subsequent request; updating the stored healthassessment data with the second collective health assessment data; andwhen the health assessment data of at least one computing instance ofthe subset of computing instances associated with the client isdetermined to be unhealthy, executing at least one remedial actioncomprising at least one of: 1) executing operations to cause thecomputing instance to be removed from the distributed computingenvironment, or 2) transmitting a notification to a computing devicethat indicates the at least one computing 31 node is unhealthy.
 2. Thecomputer-implemented method of claim 1, wherein each health assessmentapplication of the plurality of health assessment applications executeson a plurality of computing devices separate from the plurality ofcomputing instances of the distributed computing environment.
 3. Thecomputer-implemented method of claim 1, wherein an instance of thehealth assessment data in the first distributed cache comprises firsthealth assessment data obtained by a first health assessmentapplication, second health assessment data obtained by a second healthassessment application, and overall health assessment data for theinstance calculated from the first health assessment data and the secondhealth assessment data.
 4. The computer-implemented method of claim 1,further comprising identifying the subset of computing instances from athird distributed cache based at least in part on an identifierassociated with an entity associated with the request.
 5. Thecomputer-implemented method of claim 1, further comprising maintainingapplication health assessment data for each of the plurality of healthassessment applications in a fourth distributed cache.
 6. Thecomputer-implemented method of claim 1, wherein storing the firstcollective health assessment data in the second distributed cache as thestored health assessment data is based at least in part on receiving anindication that the first collective health assessment data has beenreceived.
 7. The computer-implemented method of claim 1, furthercomprising transmitting, to the health assessment application,identifiers for a particular set of computing instances, wherein thehealth assessment application probes each of the particular set ofcomputing instances for its corresponding state.
 8. A distributedcomputing system, comprising: a plurality of computing instances; aplurality of health assessment applications configured to obtaincorresponding health assessment data from a respective subset of theplurality of computing instances; a first distributed cache; a seconddistributed cache; and a software service configured to: determine, fromthe plurality of computing instances, a set of computing instances to beprobed by the plurality of health assessment applications, the set ofcomputing instances being determined based at least in part on anestimated resource consumption for obtaining respective healthassessment data corresponding to the set of computing instances and anavailable compute resource capacity of a computing component hosting aparticular health assessment application; obtain, from a healthassessment application of the plurality of health assessmentapplications, health assessment data corresponding to a computinginstance of the set of computing instances; receive a request for firstcollective health assessment data of a subset of computing instances ofthe set of computing instances, the subset of computing instances beingassociated with a client; obtain, from the first distributed cache, thefirst collective health assessment data corresponding to the subset ofcomputing instances associated with the client; provide the firstcollective health assessment data for the subset of computing instancesin response to the request; store the first collective health assessmentdata in the second distributed cache as stored health assessment data;receive a subsequent request for second collective health assessmentdata of the subset of computing instances associated with the client;obtain, from the first distributed cache, the second collective healthassessment data corresponding to the subset of computing instances;calculate change data indicative of a difference between the firstcollective health assessment data and the second collective healthassessment data; provide the change data in response to the subsequentrequest; update the stored health assessment data with the secondcollective health assessment data; and when the health assessment dataof at least one computing node of the subset of computing instancesassociated with the client is determined to be unhealthy, execute atleast one remedial action comprising at least one of 1) executingoperations to cause the computing instance to be removed from thedistributed computing environment, or 2) transmitting a notification toa computing device that indicates the at least one computing node isunhealthy.
 9. The distributed computing system of claim 8, wherein aninstance of the health assessment data in the first distributed cachecomprises first health assessment data obtained by a first healthassessment application, second health assessment data obtained by asecond health assessment application, and overall health assessment datafor the instance calculated from the first health assessment data andthe second health assessment data.
 10. The distributed computing systemof claim 8, wherein the software service is further configured to:identify the subset of computing instances from a third distributedcache based at least in part on an identifier associated with an entityassociated with the request; and maintain application health assessmentdata for each of the plurality of health assessment applications in afourth distributed cache.
 11. The distributed computing system of claim8, wherein storing the first collective health assessment data in thesecond distributed cache as the stored health assessment data is basedat least in part on receiving an indication that the first collectivehealth assessment data has been received.
 12. The distributed computingsystem of claim 8, wherein the software service is further configured totransmit, to the particular health assessment application, identifiersfor a particular set of computing instances, wherein the particularhealth assessment application probes each of the particular set ofcomputing instances for its corresponding state.
 13. A non-transitorycomputer-readable storage medium comprising executable instructionsthat, when executed with one or more processors of a computing device,cause the computing device to: determine, from a plurality of computinginstances, a set of computing instances of a distributed computingenvironment to be probed by a plurality of health assessmentapplications, the set of computing instances being determined based atleast in part on an estimated resource consumption for obtainingrespective health assessment data corresponding to the set of computinginstances and an available compute resource capacity of a computingcomponent hosting a particular health assessment application; obtain,from a health assessment application of the plurality of healthassessment applications, health assessment data corresponding to acomputing instance of the set of computing instances; receive a requestfor first collective health assessment data of a subset of computinginstances of the set of computing instances, the subset of computinginstances being associated with a client; obtain, from a firstdistributed cache, the first collective health assessment datacorresponding to the subset of computing instances associated with theclient; provide the first collective health assessment data for thesubset of computing instances in response to the request; store thefirst collective health assessment data in a second distributed cache asstored health assessment data; receive a subsequent request for secondcollective health assessment data of the subset of computing instancesassociated with the client; obtain, from the first distributed cache,the second collective health assessment data corresponding to the subsetof computing instances; calculate change data indicative of a differencebetween the first collective health assessment data and the secondcollective health assessment data; provide the change data in responseto the subsequent request; update the stored health assessment data withthe second collective health assessment data; and when the healthassessment data of at least one computing node of the subset ofcomputing instances associated with the client is determined to beunhealthy, execute at least one remedial action comprising at least oneof: 1) executing operations to cause the computing instance to beremoved from the distributed computing environment, or 2) transmitting anotification to another computing device that indicates the at least onecomputing node is unhealthy.
 14. The non-transitory computer-readablestorage medium of claim 13, wherein an instance of the health assessmentdata in the first distributed cache comprises first health assessmentdata obtained by a first health assessment application, second healthassessment data obtained by a second health assessment application, andoverall health assessment data for the instance calculated from thefirst health assessment data and the second health assessment data. 15.The non-transitory computer-readable storage medium of claim 13, whereinexecuting the instructions further causes the computing device to:identify the subset of computing instances from a third distributedcache based at least in part on an identifier associated with an entityassociated with the request; and maintain application health assessmentdata for each of the plurality of health assessment applications in afourth distributed cache.
 16. The non-transitory computer-readablestorage medium of claim 13, wherein storing the first collective healthassessment data in the second distributed cache as the stored healthassessment data is based at least in part on receiving an indicationthat the first collective health assessment data has been received. 17.The non-transitory computer-readable storage medium of claim 13, whereinexecuting the instructions further causes the computing device totransmit, to the health assessment application, identifiers for aparticular set of computing instances, wherein the health assessmentapplication probes each of the particular set of computing instances forits corresponding state.